First trimester prediction of gestational diabetes mellitus using plasma biomarkers: a case-control study

Associations between first-trimester screening biomarkers and maternal characteristics with gestational diabetes mellitus in Chinese women

Background

Gestational diabetes mellitus (GDM) can result in adverse maternal and neonatal outcomes. Predicting those at high risk of GDM and early interventions can reduce the development of GDM. The aim of this study was to examine the associations between first-trimester prenatal screening biomarkers and maternal characteristics in relation to GDM in Chinese women.

Methods

We conducted a retrospective cohort study of singleton pregnant women who received first-trimester aneuploidy and preeclampsia screening between January 2019 and May 2021. First-trimester prenatal screening biomarkers, including pregnancy-associated plasma protein A (PAPP-A), free beta-human chorionic gonadotropin, and placental growth factor (PLGF), along with maternal characteristics, were collected for analysis in relation to GDM. Receiver operating characteristic (ROC) curve and logistic regression analyses were used to evaluate variables associated with GDM.

Results

Of the 1452 pregnant women enrolled, 96 developed GDM. PAPP-A (5.01 vs. 5.73 IU/L, P < 0.001) and PLGF (39.88 vs. 41.81 pg/mL, P = 0.044) were significantly lower in the GDM group than in the non-GDM group. The area under the ROC curve of combined maternal characteristics and biomarkers was 0.73 (95% confidence interval [CI] 0.68-0.79, P < 0.001). The formula for predicting GDM was as follows: P = 1/[1 + exp (-8.148 + 0.057 x age + 0.011 x pregestational body mass index + 1.752 x previous GDM history + 0.95 x previous preeclampsia history + 0.756 x family history of diabetes + 0.025 x chronic hypertension + 0.036 x mean arterial pressure - 0.09 x PAPP-A - 0.001 x PLGF)]. Logistic regression analysis revealed that higher pregestational body mass index (adjusted odds ratio [aOR] 1.03, 95% CI 1.01 - 1.06, P = 0.012), previous GDM history (aOR 9.97, 95% CI 3.92 - 25.37, P < 0.001), family history of diabetes (aOR 2.36, 95% CI 1.39 - 4.02, P = 0.001), higher mean arterial pressure (aOR 1.17, 95% CI 1.07 - 1.27, P < 0.001), and lower PAPP-A level (aOR 0.91, 95% CI 0.83 - 1.00, P = 0.040) were independently associated with the development of GDM. The Hosmer-Lemeshow test demonstrated that the model exhibited an excellent discrimination ability (chi-square = 3.089, df = 8, P = 0.929).

Conclusion

Downregulation of first-trimester PAPP-A and PLGF was associated with the development of GDM. Combining first-trimester biomarkers with maternal characteristics could be valuable for predicting the risk of GDM.

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

Background

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

Aim

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

Methods

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

Results

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

Conclusions

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

Biochemical Markers in the Prediction of Pregnancy Outcome in Gestational Diabetes Mellitus

Background and Objectives: Gestational diabetes mellitus (GDM) may impact both maternal and fetal/neonatal health. The identification of prognostic indicators for GDM may improve risk assessment and selection of patient for intensive monitoring. The aim of this study was to find potential predictors of adverse pregnancy outcome in GDM and normoglycemic patients by comparing the levels of different biochemical parameters and the values of blood cell count (BCC) between GDM and normoglycemic patients and between patients with adverse and good outcome. Materials and Methods: Prospective clinical study included 49 patients with GDM (study group) and 44 healthy pregnant women (control group) who underwent oral glucose tolerance test (OGTT) at gestational age of 24-28 weeks. At the time of OGTT peripheral blood was taken for the determination of glucose levels, insulin, glycated hemoglobin, lipid status, homeostatic model assessment, BCC, iron and zinc metabolism, liver function, kidney function and inflammatory status. Each group was divided into two subgroups-normal and poor pregnancy outcome. Results: Higher RBC, hemoglobin concentration, hematocrit value, fasting glucose, uric acid and fibrinogen were found in GDM patients compared to control group. In GDM patients with poor pregnancy outcome values of fibrinogen, ALT, sedimentation rate, granulocyte and total leukocyte counts were elevated, while the serum level of zinc was significantly lower. Higher level of fibrinogen was found in normoglycemic patients with adverse pregnancy outcomes. ROC curve was constructed in order to assess fibrinogen's biomarker potential. The established AUC value for diagnostic ROC was 0.816 (p < 0.001, 95% CI 0.691-0.941), while the AUC value for assessing fibrinogen's potential to predict poor pregnancy outcome in GDM was 0.751 (p = 0.0096, 95% CI 0.561-0.941). Conclusions: The results of our study demonstrated that the best prognostic potential in GDM showed inflammation related parameters, identifying fibrinogen as a parameter with both diagnostic and prognostic ability.

The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence

Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.

First Trimester Placental Biomarkers for Pregnancy Outcomes

The placenta plays a key role in several adverse obstetrical outcomes, such as preeclampsia, intrauterine growth restriction and gestational diabetes mellitus. The early identification of at-risk pregnancies could significantly improve the management, therapy and prognosis of these pregnancies, especially if these at-risk pregnancies are identified in the first trimester. The aim of this review was to summarize the possible biomarkers that can be used to diagnose early placental dysfunction and, consequently, at-risk pregnancies. We divided the biomarkers into proteins and non-proteins. Among the protein biomarkers, some are already used in clinical practice, such as the sFLT1/PLGF ratio or PAPP-A; others are not yet validated, such as HTRA1, Gal-3 and CD93. In the literature, many studies analyzed the role of several protein biomarkers, but their results are contrasting. On the other hand, some non-protein biomarkers, such as miR-125b, miR-518b and miR-628-3p, seem to be linked to an increased risk of complicated pregnancy. Thus, a first trimester heterogeneous biomarkers panel containing protein and non-protein biomarkers may be more appropriate to identify and discriminate several complications that can affect pregnancies.

A first trimester prediction model and nomogram for gestational diabetes mellitus based on maternal clinical risk factors in a resource-poor setting

BACKGROUND

The implementation of universal screening for Gestational Diabetes Mellitus (GDM) is challenged by several factors key amongst which is limited resources, hence the continued reliance on risk factor-based screening. Effective identification of high-risk women early in pregnancy may enable preventive intervention. This study aimed at developing a GDM prediction model based on maternal clinical risk factors that are easily assessable in the first trimester of pregnancy in a population of Nigerian women.

METHODS

This was a multi-hospital prospective observational cohort study of 253 consecutively selected pregnant women from which maternal clinical data was collected at 8-12 weeks gestational age. Diagnosis of GDM was made via a one-step 75-gram Oral Glucose Tolerance Test (OGTT) at 24-28 weeks of gestation. A GDM prediction model and nomogram based on selected maternal clinical risk factors was developed using multiple logistic regression analysis, and its performance was assessed by Receiver Operator Curve (ROC) analysis. Data analysis was carried out using Statistical Package for Social Sciences (SPSS) version 25 and Python programming language (version 3.0).

RESULTS

Increasing maternal age, higher body mass index (BMI), a family history of diabetes mellitus in first-degree relative and previous history of foetal macrosomia were the major predictors of GDM. The model equation was: LogitP = 6.358 - 0.066 × Age - 0.075 × First trimester BMI - 1.879 × First-degree relative with diabetes mellitus - 0.522 × History of foetal macrosomia. It had an area under the receiver operator characteristic (ROC) curve (AUC) of 0.814 (95% CI: 0.751-0.877; p-value < 0.001), and at a predicted probability threshold of 0.745, it had a sensitivity of 79.2% and specificity of 74.5%.

CONCLUSION

This first trimester prediction model reliably identifies women at high risk for GDM development in the first trimester, and the nomogram enhances its practical applicability, contributing to improved clinical outcomes in the study population.

The effect of circulating adiponectin levels on incident gestational diabetes mellitus: systematic review and meta‑analysis

BACKGROUND

To quantitatively synthesize evidence from prospective observational studies regarding the mean levels of circulating adiponectin in patients with gestational diabetes mellitus (GDM) and the association between adiponectin levels and GDM risk.

METHODS

PubMed, EMBASE and Web of Science were searched from their inception until November 8th, 2022, for nested case-control studies and cohort studies. Random-effect models were applied to the synthesized effect sizes. The difference in circulating adiponectin levels between the GDM and control groups was measured using the pooled standardized mean difference (SMD) and 95% confidence interval (CI). The relationship between circulating adiponectin levels and GDM risk was examined using the combined odds ratio (OR) and 95% CI. Subgroup analyses were performed according to the study continent, GDM risk in the study population, study design, gestational weeks of circulating adiponectin detection, GDM diagnostic criteria, and study quality. Sensitivity and cumulative analyses were performed to evaluate the stability of the meta-analysis. Publication bias was assessed by funnel plots and Egger's test.

RESULTS

The 28 studies included 13 cohort studies and 15 nested case-control studies, containing 12,256 pregnant women in total. The mean adiponectin level in GDM patients was significantly lower than in controls (SMD = -1.514, 95% CI = -2.400 to -0.628, p = .001, I² = 99%). The risk of GDM was significantly decreased among pregnant women with increasing levels of circulating adiponectin (OR = 0.368, 95% CI = 0.271-0.500, p < .001, I²=83%). There were no significant differences between the subgroups.

CONCLUSIONS

Our findings indicate that increasing circulating adiponectin levels were inversely associated with the risk of GDM. Given the inherent heterogeneity and publication bias of the included studies, further well-designed large-scale prospective cohort or intervention studies are needed to confirm our finding.

Associations of early pregnancy serum uric acid levels with risk of gestational diabetes and birth outcomes: a retrospective cohort study

BACKGROUND

Previous evidence suggests that higher blood uric acid (UA) levels are associated with adverse cardiovascular outcomes during pregnancy and subsequent birth outcomes. However, it has been relatively unclear whether these associations persist in normotensive pregnant women.

METHODS

The study was based on a retrospective analysis of 18,250 mother-infant pairs in a large obstetric center in China. Serum UA concentrations in early pregnancy (median: 17.6, IQR: 16.3, 18.6 gestational weeks) were assessed. Hyperuricemia was defined as ≥ one standard deviation (SD) of the reference value for the corresponding gestational age. Outcomes of gestational diabetes mellitus (GDM), preterm birth (PB), low birth weight (LBW), macrosomia, small for gestational age (SGA) and large for gestational age (LGA) were extracted from the medical records.

RESULTS

The mean maternal UA level was 0.22 ± 0.05 mmol/L, and 2,896 (15.9%) subjects had hyperuricemia. After adjustment for several covariates, UA was associated with several adverse outcomes. The ORs (95%CI) per one SD increase in serum UA concentration were 1.250 (1.136, 1.277) for GDM, 1.137 (1.060, 1.221) for PB, 1.134 (1.051, 1.223) for LBW, and 1.077 (1.020, 1.137) for SGA, respectively. Similar adverse associations were found between hyperuricemia and GDM, PB (ORs: 1.394 and 1.385, P < 0.001), but not for LBW, macrosomia, SGA, and LGA. Adverse associations tended to be more pronounced in subjects with higher BMI for outcomes including PB, LBW, and SGA (P interaction = 0.001-0.028).

CONCLUSION

Higher UA levels in early pregnancy were associated with higher risk of GDM, PB, LBW, and SGA in normotensive Chinese women.

The Association of the Triglyceride and Muscle to Fat Ratio During Early Pregnancy with the Development of Gestational Diabetes Mellitus

Objective

This study explored the association between metabolic factors and body composition during the first trimester of gestational diabetes mellitus (GDM).

Methods

This prospective study recruited pregnant women in their first trimester. Clinical information and glucose and lipid measurements were collected, and body composition was assessed using multifrequency bioelectrical impedance analysis. GDM was diagnosed on the basis of an oral glucose tolerance test at 24-28 gestational week. Factors related to GDM were investigated using correlation, and risk ratios (RRs) and 95% CIs of potential risk factors with GDM were estimated using Poisson regression. The area under the receiver operating characteristic (ROC) curve was used to determine predictive effects.

Results

59/302 women (19.5%) developed GDM. Older (RR 1.076, 95% CI 1.005-1.152), higher body mass index (BMI) before pregnancy (pre-BMI) (RR 1.012, 95% CI 1.005-1.063), triglycerides (RR 4.052, 95% CI 1.641-6.741), and lower skeletal muscle mass (SMM) to fat mass (FM) ratio (SMM/FM) (RR 0.213, 95% CI 0.051-0890) in the first trimester, and family history of type 2 diabetes (RR 1.496, 95% CI 1.014-2.667) significantly associated with the risk of GDM, but neither fasting plasma glucose nor glycated albumin was associated with GDM. The combined multivariate prediction model achieved good discrimination with an AUC of 0.806 (95% CI 0.737-0.895, P<0.001). According to ROC curve, the cut-off values of TG and SMM/FM were 0.925 mmol/L and 1.305.

Conclusion

Reduced SMM/FM and elevated triglyceride (TG) levels in the first trimester are associated with GDM development, and should be screened in early pregnancy to identify high-risk subjects for GDM.

Serum uric acid and the risk of gestational diabetes mellitus: a systematic review and meta-analysis

AIMS

Serum uric acid (SUA) is considered as a risk factor for gestational diabetes mellitus (GDM). However, current studies showed inconsistent results. This study aimed to explore the relationship between SUA levels and GDM risk.

METHODS

Eligible studies were retrieved from PubMed, Web of Science, Embase, China National Knowledge Infrastructure, and Wanfang databases up to November 1, 2022. The pooled standardized mean difference (SMD) and 95% confidence interval (CI) were used to represent the difference in SUA levels between GDM women and controls. The combined odds ratios (OR) and 95% CI were applied to assess association between SUA levels and GDM risk. Subgroup analyses were conducted on study continents, design, and quality, detection time of SUA, and GDM diagnostic criteria.

RESULTS

Totally 11 studies including five case-control and six cohort studies, in which 80,387 pregnant women with 9815 GDM were included. The overall meta-analysis showed that the mean SUA level in GDM group was significantly higher than in controls (SMD = 0.423, 95%CI = 0.019-0.826, p = .040, I² = 93%). Notably, pregnant women with elevated levels of SUA had a significantly increased risk of GDM (OR = 1.670, 95%CI = 1.184-2.356, p = .0035, I² = 95%). Furthermore, subgroup analysis performed on the detection time of SUA showed a significant difference in the association between SUA and GDM risk within different trimesters (1st trimester: OR = 3.978, 95%CI = 2.177-7.268; 1st to 2nd trimester: OR = 1.340, 95%CI = 1.078-1.667; p between subgroups <.01).

CONCLUSIONS

Elevated SUA was positively associated with GDM risk, particularly in the 1st trimester of pregnancy. Further studies with high quality are required to validate the findings of this study.

Elevated Serum Uric Acid Is Associated With Gestational Diabetes Mellitus: An Observational Cohort Study

CONTEXT

Elevated serum uric acid may be closely related to the occurrence of gestational diabetes mellitus (GDM).

OBJECTIVE

We aimed to elucidate the relationship between changes in serum uric acid before 24 weeks of gestation and the risk of GDM and associated adverse pregnancy outcomes and provide clinical epidemiological evidence for the involvement of uric acid in the etiology of GDM.

METHODS

We conducted a retrospective cohort study of 23 843 singleton pregnant women between February 2018 and June 2022. The exposure factor was serum uric acid before 24 weeks of gestation, primary outcome was gestational diabetes diagnosed at 24 to 28 weeks of gestation, and secondary outcomes were GDM A2 (GDM requiring pharmacotherapy), GDM combined with pre-eclampsia, preterm delivery, and large for gestational age infants. Adjusted risk ratios (RRs) were calculated using multivariate predictive marginal proportions from logistic regression models.

RESULTS

Among 23 843 singleton pregnant women, 3204 (13.44%) were diagnosed with GDM at 24 to 28 weeks of gestation, and elevated uric acid before 24 weeks of gestation was strongly associated with the risk of GDM. Compared with uric acid <240 µmol/L, the RR for GDM was 1.43 (95% CI 1.29-1.56) when uric acid was between 240 and 300 µmol/L; when uric acid was >300 µmol/L, the RR for GDM was 1.82 (95% CI 1.55-2.15). In secondary outcomes uric acid had a similar relationship with GDM A2, preterm birth, and GDM combined with pre-eclampsia.

CONCLUSION

Elevated uric acid levels before 24 weeks of gestation are associated with subsequent GDM; the best time to test for uric acid is before 18 weeks of gestation. Pregnant women with low and intermediate risk for GDM development may benefit more from serum uric acid measurements before 18 weeks of gestation.

Total alkaline phosphatase levels by gestational age in a large sample of pregnant women

INTRODUCTION

Total alkaline phosphatase (tALP) levels rise physiologically in maternal serum during pregnancy, and excessively so in certain conditions. However, current reference values are dated, nonlinear, and based on small samples. Factors related to variation in tALP remain unexplained. Thus, our goals in this study were to establish a physiological development curve for tALP within low-risk pregnancies and to evaluate the factors influencing tALP values.

METHODS

This was a single-center, retrospective, observational study. All patients who delivered a live singleton infant at our center from January 1, 2011 to May 31, 2019, and had a tALP assay during pregnancy, were included regardless of the gestational age at which the assay was conducted.

RESULTS

A total of 2415 pregnancies were included. Median tALP decreased during the first trimester, it increased slightly during the second trimester, and then increased sharply during the third trimester. Factors associated with a significant increase in tALP were chronic histiocytic intervillositis, cholestasis, multiple pregnancies, liver disease, preeclampsia, smoking, and low weight for gestational age. Conversely, gestational diabetes was associated with a discrete decrease in tALP.

DISCUSSION

Our large sample allowed establishment of tALP reference curves based on gestational age. To interpret these results more thoroughly, factors that influence tALP rates should be further scrutinized.

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

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

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 sex hormone-binding globulin predicts gestational diabetes mellitus in a population of Nigerian women

There has been a steady rise in the disease burden of Gestational Diabetes Mellitus (GDM) in the sub-Saharan African region over time. Diagnostic testing for GDM is currently recommended at 24 - 28 weeks of gestation, leaving a narrow window for intervention before delivery. Hence the need for early prediction and preventive intervention. The performance of first trimester serum sex hormone-binding globulin (SHBG) assay as a predictor of GDM was determined by binary logistic regression. Women with GDM (n = 49) had a significantly lower mean first trimester SHBG level (104.7 ± 61.6 nmol/L) than did those without GDM (n = 180; 265.2 ± 141.5 nmol/L; p < .001). First trimester SHBG was significantly negatively correlated (r_pb = -0.460, p value = <.001) with subsequent development of GDM and an area under receiver operator characteristics (ROC) curve of 0.874 (p < .001). A cut-off value of 158.0 nmol/L predictive of GDM had a diagnostic sensitivity of 81.5%, a specificity of 80.1%, and an overall diagnostic efficiency of 80.3%.IMPACT STATEMENTWhat is already known on this subject? GDM is associated with high risk of various complications and is commonly diagnosed at 24-28 weeks of gestation, leaving a narrow window for intervention. The performance of current maternal clinical and demographic risk factor-based prediction approaches is unreliable. Thus, more favourable prediction approaches need to be developed. Previous studies have suggested that SHBG, a readily assessable marker, has potential to predict GDM; however, these studies have mostly involved Caucasian and other non-African populations.What the results of this study add? SHBG may serve as a reliable first trimester screening tool for GDM development in Nigerian women with singleton pregnancies. This study demonstrates that first trimester SHBG can predict GDM development in sub-Saharan African women despite racial, ethnic and geographical differences.What are the implications of these findings for clinical practice and/or further research? Effective first trimester prediction of GDM using SHBG may enable preventive interventions, thereby mitigating the high burden of the disease in the sub-Saharan African region. It may also provide relevant information that may guide adaptation of current management guidelines to ensure effective management of GDM in the region.

A clinical model and nomogram for early prediction of gestational diabetes based on common maternal demographics and routine clinical parameters

AIM

We aimed to develop a risk prediction model for gestational diabetes mellitus (GDM) based on the common maternal demographics and routine clinical variables in Chinese population.

METHODS

Individual information was collected from December 2018 to October 2019 by a pretested questionnaire on demographics, medical and family history, and lifestyle factors. Multivariable logistic regression was performed to establish a predictive model for GDM by variables in pre- and early pregnancy. The consistency and discriminative validity of the model were evaluated by Hosmer-Lemeshow goodness-of-fit testing and ROC curve analysis. Internal validation was appraised by fivefold cross-validation. Clinical utility was assessed by decision curve analysis.

RESULTS

Total 3263 pregnant women were included with 17.2% prevalence of GDM. The model equation was: LogitP = -11.432 + 0.065 × maternal age (years) + 0.061 × pre-pregnancy BMI (kg/m² ) + 0.055 × weight gain in early pregnancy (kg) + 0.872 × history of GDM + 0.336 × first-degree family history of diabetes +0.213 × sex hormone usages during pre- or early pregnancy + 1.089 × fasting glucose (mmol/L) + 0.409 × triglycerides (mmol/L) + 0.082 × white blood cell count (109/L) + 0.669 × positive urinary glucose. Homer-Lemeshow goodness-of-fit testing indicated a good consistency between predictive and actual data (p = 0.586). The area under the ROC curve (AUC) was 0.720 (95% CI: 0.697 ~ 0.744). Cross-validation suggested a good internal validity of the model. A nomogram has been made to establish an easy to use scoring system for clinical practice.

CONCLUSIONS

The predictive model of GDM exhibited well acceptable predictive ability, discriminative performance, and clinical utilities. The project was registered in clinicaltrial.gov.com with identifier of NCT03922087.

The reduced serum concentrations of β-arrestin-1 and β-arrestin-2 in pregnancies complicated with gestational diabetes mellitus

OBJECTIVE

This study aimed to analyze maternal serum β-arrestin-1 and β-arrestin-2 concentrations in pregnant women complicated with gestational diabetes mellitus (GDM) and compare them with the normoglycemic uncomplicated healthy control group.

METHODS

A prospective case-control study was conducted, including pregnant women complicated with GDM between 15 February 2021, and 31 July 2021. We recorded serum β-arrestin-1 and β-arrestin-2 concentrations of the participants. Receiver operating characteristic (ROC) curves were used to describe and compare the performance of diagnostics value of variables β-arrestin-1, and β-arrestin-2.

RESULTS

The mean β-arrestin-1 and β-arrestin-2 levels were found to be significantly lower in the GDM group (41.0 ± 62.8 ng/mL, and 6.3 ± 9.9 ng/mL) than in the control group (93.1 ± 155.4 ng/mL, and 12.4 ± 17.7, respectively, p < .001). When we analyze the area under the ROC curve (AUC), maternal serum β-arrestin-1 and β-arrestin-2 levels can be considered a statistically significant parameter for diagnosing GDM. β-arrestin-1 had a significant negative correlation with fasting glucose (r = -0.551, p < .001), plasma insulin levels (r = -0.522, p < .001), HOMA-IR (r = -0.566, p < .001), and HbA1C (r = -0.465, p < .001). β-arrestin-2 was significantly negatively correlated with fasting glucose (r = -0.537, p < .001), plasma insulin levels (r = -0.515, p < .001), HOMA-IR (r = -0.550, p < .001), and HbA1C (r = -0.479, p < .001).

CONCLUSION

β-arrestin 1 and β-arrestin 2 could be utilized as biomarkers in the diagnosis of GDM. The novel therapeutic strategies targeting these β-arrestins may be designed for the GDM treatment.

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.

Effect of gestational diabetes mellitus on lipid profile: A systematic review and meta-analysis

Gestational diabetes mellitus (GDM) can have adverse effects on pregnancy. GDM is associated with changes in the lipid profile of pregnant women. Finding out the early ways to diagnose GDM can prevent the adverse outcomes. This meta-analysis study aimed to determine the effect of GDM on lipid profile. PubMed, ProQuest, Web of Science, Scopus, Science Direct, Google Scholar, and ClinicalTrial were systematically searched for published articles relating to GDM until 2021 according to PRISMA guidelines. Newcastle Ottawa scale was used to assess the quality of the studies. Thirty-three studies with a sample size of 23,792 met the criteria for entering the meta-analysis. Pooled standardized mean difference (SMD) for total cholesterol (TC) and triglyceride (TG) was 0.23 mg/dL (95% CI: 0.11–0.34) and 1.14 mg/dL (95% CI: 0.91–1.38), respectively. The mean of TC and TG in people with GDM was higher than that in normal pregnant women. A similar pattern was observed for the very low-density lipoprotein (VLDL) and TG/high-density lipoprotein (HDL) ratio, with pooled SMD of 0.99 mg (95% CI: 0.71–1.27) and 0.65 mg (95% CI: 0.36–0.94), respectively. Pooled SMD for HDL was −0.35 mg/dL (95% CI: −0.54 to −0.16), women with GDM had a mean HDL lower than normal pregnant women. Although pooled SMD was higher for low-density lipoprotein (LDL) in the GDM group, this difference was not significant (0.14 [95% CI: −0.04 to 0.32]). Of all the lipid profiles, the largest difference between the GDM and control groups was observed in TG (SMD: 1.14). Elevated serum TG had the strongest effect on GDM. Higher levels of TC, LDL, VLDL, and TG/HDL ratio, and lower level of HDL were exhibited in GDM group. So, these markers can be considered as a reliable marker in the diagnosis of GDM.

The Role of Afamin and Other Trace Elements in the Prediction of GDM: a Tertiary Center Experience

The objective of this study was to evaluate the utility of first trimester maternal serum afamin levels together with vitamin E and various elements (zinc, copper, selenium, and magnesium) for the prediction of gestational diabetes mellitus (GDM). All pregnant women between 11th and 14th weeks of gestation admitted for combined test were asked to participate in the study. A total of 797 women gave permission to participate in the study between January and September 2019. Serum was obtained by centrifugation and samples were frozen and kept at - 80 °C. For final analysis, forty-three GDM patients and forty-four healthy controls were selected. Levels of afamin, vitamin E, zinc, copper, selenium, and magnesium were compared between groups. The mean levels of afamin were found to be higher in women with GDM without statistical significance (63.69 ± 82.33 vs 44.25 ± 32.25 mg/L, p = 0.149). Vitamin E levels were found to be higher in women with GDM compared to controls without any statistical significance (5.04 ± 5.33 vs 4.47 ± 3.83 μg/mL, p = 0.568). While first trimester copper concentrations were higher among diabetic women (187.26 ± 34.78 vs 175.17 ± 30.40 μg/L, p = 0.088), this was not statistically significant. The other element levels (zinc, selenium, and magnesium) were found to be similar between the two groups (p = 0.624, p = 0.088, p = 0.254, p = 0.872, respectively). The results of our study demonstrated that mean levels of afamin, vitamin E, and copper were higher in women with GDM compared to controls. Additionally, first trimester maternal zinc, selenium, and magnesium levels were similar between diabetic and healthy pregnant women. However, more studies are needed to clarify the relationship between blood trace concentrations and GDM.

Weight Loss Prior to Pregnancy and Early Gestational Glycemia: Prepare, a Randomized Clinical Trial

CONTEXT

Women with elevated body mass index are encouraged to lose weight before pregnancy, but no trials have tested the effects of prepregnancy weight loss on risk of developing gestational diabetes.

OBJECTIVE

This work aims to determine whether prepregnancy weight loss improved the early metabolic environment as measured by early gestational diabetes diagnosis.

METHODS

This was a secondary analysis of a pragmatic randomized clinical trial conducted between May 2015 and October 2019 in an integrated health system that encouraged first-trimester gestational diabetes screening for high-risk women, including those with obesity. Women aged 18 to 40 years with a body mass index (BMI) greater than or equal to 27 who were planning pregnancy were randomly assigned to a behavioral weight loss intervention or usual care. Clinical care decisions and data collection were blind to condition assignment. We compared rates of diagnosis with gestational diabetes in early pregnancy between the groups using logistic regression.

RESULTS

Of 326 participants, 168 (89 in the intervention and 79 in usual care) had singleton pregnancies during the study period. At baseline, mean age was 31.3 ± 3.5 years and BMI was 34.8 ± 5.8. Fifty-nine (66%) intervention participants and 57 (72%) usual care participants underwent early screening. Among those, intervention participants were 73% less likely to be diagnosed with gestational diabetes than usual care participants (adjusted odds ratio [aOR], 0.27; 95% CI, 0.09-0.80). There was no difference in diagnosis of gestational diabetes in later pregnancy (aOR, 1.08; 95% CI, 0.41-2.81).

CONCLUSION

Participation in a prepregnancy weight loss intervention led to lower rates of gestational diabetes diagnosis in early pregnancy. This suggests positive effects of prepregnancy weight loss on the early metabolic environment, a critical factor in offspring metabolic risk.

Serum uric acid in early pregnancy and risk of gestational diabetes mellitus: a cohort study of 85,609 pregnant women

AIMS

. - Higher serum uric acid (UA) has been associated with increased risk of type 2 diabetes mellitus. This cohort study examined whether there are any associations between serum UA in early pregnancy and the subsequent risk of gestational diabetes mellitus (GDM).

METHODS

. - This cohort study was conducted in Shanghai, China, and included 85,609 pregnant women. Generalised additive models were used to estimate the associations of serum UA with risk of GDM.

RESULTS

. - The prevalence of GDM was 14.0% (11,960/85,609). Non-linear associations between serum UA and GDM risk were observed and these associations varied by gestational ages. Only elevated serum UA levels at 13-18 weeks gestation was associated with substantially increased risk of GDM. Analysis by UA quintiles at 13-18 weeks gestation showed the odds ratios for GDM were 1.11 (95%CI, 1.03-1.20) for the second, 1.27 (95%CI, 1.17-1.37) for the third, 1.37 (95%CI, 1.27-1.48) for the fourth and 1.70 (95%CI, 1.58-1.84) for the fifth quintile of serum UA in comparison with the first quintile. Stratified analysis showed the associations of serum UA with GDM were stronger among pregnant women aged 35 years or older.

CONCLUSION

. - We found higher serum UA at 13-18 gestational weeks was a risk factor for GDM. Our findings provide new evidence for the role of serum UA in the prevention and early intervention of GDM, and highlighted the need for monitoring serum UA at 13-18 gestational weeks.

A risk-prediction model using parameters of maternal body composition to identify gestational diabetes mellitus in early pregnancy

BACKGROUND

Accurate early risk-prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated maternal risk-factors and parameters of body-composition to develop a prediction model for GDM in early gestation.

METHODS

A prospective observational study was undertaken. Pregnant women aged between 18 and 50 y of age with gestational age between 10 and 16 weeks were included in the study. Women aged ≤18 y, twin-pregnancies, known foetal anomaly or pre-existing condition affecting oedema status were excluded. 8-point-skinfold thickness (SFT), mid-upper-arm-circumference (MUAC), waist, hip, weight and ultrasound measurements of subcutaneous (SAT) and visceral abdominal-adipose (VAT) were measured. Oral-glucose-tolerance-test (OGTT) for GDM diagnosis was undertaken at 28 weeks gestation. Binomial logistic-regression models were used to predict GDM. ROC-analysis determined discrimination and concordance of model and individual variables.

RESULTS

188 women underwent OGTT at ~28 weeks gestation. 20 women developed GDM. BMI (24.7 kg m^-2 (±6.1), 29.9 kg m^-2 (±7.8), p = 0.022), abdominal SAT(1.32 cm (CI 1.31, 1.53), 1.99 cm (CI 1.64, 2.31), p = 0.027), abdominal VAT(0.78 cm (CI 0.8, 0.96), 1.41 cm (CI 1.11, 1.65), p = 0.002), truncal SFT (84.8 mm (CI 88.2, 101.6), 130.4 mm (CI 105.1, 140.1), p = 0.010), waist (79.8 cm (CI 80.3, 84.1), 90.3 cm (CI 85.9, 96.2), p = 0.006) and gluteal hip (94.3 cm (CI 93.9, 98.0), 108.6 cm (CI 99.9, 111.6), p = 0.023) were higher in GDM vs. non-GDM. After screening variables for inclusion into the multivariate model, family history of diabetes, previous perinatal death, overall insulin resistant condition, abdominal SAT and VAT, 8-point SFT, MUAC and weight were included. The combined multivariate prediction model achieved an excellent level of discrimination, with an AUC of 0.860 (CI 0.774, 0.945) for GDM.

CONCLUSIONS

An early gestation risk prediction model, incorporating known risk-factors, and parameters of body-composition, accurately identify pregnant women in their first-trimester who developed GDM later on in gestation. This methodology could be used clinically to identify at-risk pregnancies, and target specific treatment through referred services to those mothers who would most benefit.

Early Pregnancy Glycemic Levels in Non-Diabetic Women and Pregnancy Outcome: A Retrospective Cross-Sectional Study

Background

Maternal fasting blood sugar (FBS) variations within normal range and lower than that in diabetes mellitus (DM) may be associated with adverse feto-maternal outcomes.

Objective

To find out if a rise of maternal FBS level above 80 but still below 120 mg/dL (group 2) has an influence on feto-maternal outcomes compared with a maternal FBS below 80 mg/dL (group 1).

Methods

Retrospective cross-sectional study. FBS was measured at the booking visit. Subjects whose FBS was measured before 20 weeks were categorized according to their FBS (>80 mg/dL or ≤80 mg/dL) and correlation between FBS levels in the two groups with several parameters were tested.

Results

Group 1 (130 healthy pregnant women) and group 2 (88 healthy pregnant women) did not show a statistical difference in age or BMI. More statistically significant cases were diagnosed with GDM in group 2 than in group 1 (39.8% vs 16.9%, P value 0.000). More cases that needed pharmacological intervention in the form of metformin or insulin or both were seen in group 2 than in group 1 (p value 0.007 and 0.061, respectively). More but not statistically significant polyhydramnios was seen more in group 2 than in group 1 (9.1% vs 3.1%, p value 0.056). There was no statistically significant difference between the 2 groups in relation to all other fetomaternal outcome parameters that were studied.

Conclusion

Raised maternal fasting blood glucose level (80–120 mg/dL) in healthy primigravid women in early pregnancy was associated with significant diagnosis of gestational diabetes mellitus and need for pharmacological intervention. An association was found with polyhydramnios but this was not statistically significant. No influence was found on preterm birth, fetal weight, mode of delivery or APGAR score. More attention should be given to FBS levels early in pregnancy to reduce the risk for later complications.

Identification of diagnostic cytosine-phosphate-guanine biomarkers in patients with gestational diabetes mellitus via epigenome-wide association study and machine learning

OBJECTIVE

To explore gestational diabetes mellitus (GDM) diagnostic markers and establish the predictive model of GDM.

METHODS

We downloaded the DNA methylation data of GSE70453 and GSE102177 from the Gene Expression Omnibus database. Epigenome-wide association study (EWAS) was performed to analyze the relationship between cytosine-phosphate-guanine (CpG) methylation and GDM. And then the logistic regression models were constructed, with the β-values of CpG sites as predictor variable and the GDM occurrence as binary outcome variable. Data from GSE70453 served as training sets and data from GSE102177 served as verification sets.

RESULTS

The EWAS and overlap analysis identified nine-shared significant CpGs in the two DNA methylation data sets. Remarkably, these nine CpGs were differently methylated in GDM samples compared to their matched normal specimens, among which five fully methylated CpGs were finally selected. Importantly, we established a binary logistic regression model based on the above five CpGs, in which cg11169102, cg21179618 and cg21620107 were critical. Hence, we further built a logistic regression model by using the three CpGs and found that the area under the curve was 0.8209. The validation of the model by using the verification sets indicated the area under the curve was 0.8519.

CONCLUSIONS

We identified potential CpG biomarkers for the diagnosis of gestational diabetes mellitus patients through using EWAS and Logistic regression models in combination.

Quantitative Comparative Proteomics Reveals Candidate Biomarkers for the Early Prediction of Gestational Diabetes Mellitus: A Preliminary Study

AIM

To identify differentially expressed proteins (DEPs) in 1^st trimester maternal plasma between pregnant women at risk for gestational diabetes mellitus (GDM) and uncomplicated controls.

MATERIALS AND METHODS

First-trimester plasma from five women who developed GDM and five from non-diabetic ones were analyzed using isobaric tag for relative and absolute quantitation - labeled proteomics. Enzyme-linked immunosorbent assay was further applied in an independent cohort of 25 GDM cases and 25 controls for verification.

RESULTS

Prenylcysteine oxidase 1 (PCYOX1), beta-ala-his dipeptidase (CNDP1), extracellular matrix protein 1 (ECM1), basement membrane-specific heparan sulfate proteoglycan core protein (HSPG2), thrombospondin-4 (TSP-4) demonstrated significant differences in expression between the two groups (p<0.05). DEPs are mainly associated with complement and coagulation cascades.

CONCLUSION

The reported plasma proteomic changes represent potential biomarkers for the early identification of women at risk for GDM. Future studies using larger and more diverse cohorts are necessary to assess the clinical utility of these findings.

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.

Maternal lipid levels in pregnant women without complications in developing risk of large for gestational age newborns: a study of meta-analysis

Background: Circulating into foetal circulation across the placental barrier, abnormal maternal serum lipids predispose neonates to metabolic dysfunction and thereafter affect the steroid metabolism and functions of extra-embryonic foetal tissues. Methods: A systematic review was conducted by searching PubMed-MEDLINE and the Cochrane library between January 2010 and January 2020. The included studies were English case control studies that described original data on at least one raw lipid measurement during pregnancy in healthy women who delivered large for gestational age (LGA) newborns and in healthy women with non-LGA newborns. The data extracted from 12 studies were pooled, and the weighted mean difference (WMD) in lipid levels was calculated using random effects models. A meta-analysis was performed to identify sources of heterogeneity and to describe the significant value of the collected studies. Results: Of 649 published articles identified, a total of 12 met the inclusion criteria . Compared with women who had non-LGA newborns, those who had LGA newborns had significantly higher triglyceride (TG) levels (WMD = 0.28, 95% CI -0.02 to 0.54) and lower high density lipoprotein cholestrol (HDL-C) levels (WMD = 0.08, 95% CI -0.13 to -0.03), but not have significantly lower high-density lipoprotein cholesterol (LDL-C) levels. Moreover, the levels of total cholesterol, low-density lipoprotein cholesterol, and very low density lipoprotein cholesterol (VLDL-C) were inconsistent between both groups. Conclusions: High levels of TG and low levels of HDL-C could cause births of LGA newborns whereas maternal serum of TC, LDL-C and VLDL-C cannot be used as predictor of LGA.

Maternal lipid levels in pregnant women without complications in developing risk of large for gestational age newborns: a meta-analysis

Background: Circulating into foetal circulation across the placental barrier, abnormal maternal serum lipids predispose neonates to metabolic dysfunction and thereafter affect the steroid metabolism and functions of extra-embryonic foetal tissues. Methods: A systematic review was conducted by searching PubMed-MEDLINE and the Cochrane library between January 2010 and January 2020. The included studies were English case control studies that described original data on at least one raw lipid measurement during pregnancy in healthy women who delivered large for gestational age (LGA) newborns and in healthy women with non-LGA newborns. The data extracted from 12 studies were pooled, and the weighted mean difference (WMD) in lipid levels was calculated using random effects models. A meta-analysis was performed to identify sources of heterogeneity and to describe the significant value of the collected studies. Results: Of 643 publications identified, a total of 12 met the inclusion criteria . Compared with women who had non-LGA newborns, those who had LGA newborns had significantly higher triglyceride (TG) levels (WMD = 0.28, 95% CI -0.02 to 0.54) and lower high density lipoprotein cholestrol (HDL-C) levels (WMD = 0.08, 95% CI -0.13 to -0.03), but not have significantly lower high-density lipoprotein cholesterol (LDL-C) levels. Moreover, the levels of total cholesterol, low-density lipoprotein cholesterol, and very low density lipoprotein cholesterol (VLDL-C) were inconsistent between both groups. Conclusions: High levels of TG and low levels of HDL-C could cause births of LGA newborns whereas maternal serum of TC, LDL-C and VLDL-C cannot be used as predictor of LGA.

Soluble human leukocyte antigen-G evaluation in pregnant women with gestational diabetes mellitus

Background

Gestational diabetes mellitus is any degree of glucose intolerance with onset or first recognition occurring late in second trimester and third trimester of pregnancy. It constitutes a greater impact on diabetes epidemic as it carries a major risk for developing type 2 diabetes mellitus to the mother and her fetus later in life. human leukocyte antigen (HLA)-G is a class Ib gene presents in the human major histocompatibility complex (MHC). HLA-G has an important role for mother and fetus tolerance during pregnancy, also in the pancreatic islet cells protection. This is a case-control study, measuring serum HLA-G levels by ELISA in 60 pregnant women with gestational diabetes compared with 36 normal pregnant women.

Results

HLA-G levels were significantly high in pregnant women with gestational diabetes mellitus (GDM) in contrast to women with normal pregnancy (P = 0.001).

Conclusion

Women with GDM had significantly higher levels of soluble HLA-G than women without GDM, suggesting that HLA-G molecule is among the factors for regulation and control of the immune response and the induction of tolerance. Soluble HLA-G could be considered an important follow-up investigation for all pregnant primary health care for early detection of gestational diabetes.

Proteome profiling of gestational diabetes mellitus at 16-18 weeks revealed by LC-MS/MS

BACKGROUND

The practices used to diagnose gestational diabetes mellitus (GDM) could only be carried out around the time of detectable symptoms, and predictive capacity is little.

METHODS

LC-MS/MS was conducted to explore overview proteomics for GDM complicated pregnant woman at 16-18 gestation weeks, while normal pregnant for control. Enzyme-linked immunosorbent assay was further applied in an independent cohort of 15 GDM cases and 15 controls for verification.

RESULTS

The results indicated that 24 protein expression levels were significantly changed in GDM group samples, and inflammation, oxidative stress, insulin resistance, blood coagulation, and lipid homeostasis were associated with GDM. The abnormal expression of CRP and IGFBP2 was verified in the first-trimester maternal plasma in women who subsequently developed GDM.

CONCLUSIONS

This study not only identified 24 potential predictive biomarkers for GDM also provided a global overview of protein rearrangements induced by GDM.

Increased risk of gestational diabetes mellitus in women with higher prepregnancy ambient PM2.5 exposure

BACKGROUND

Air pollution is a serious environmental problem in China. This study was designed to investigate whether exposure to particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM_2.5) before pregnancy is associated with gestational diabetes mellitus (GDM) and fasting glucose in China.

METHODS

We recruited subjects and collected clinical data from the Nanjing Maternity and Child Health Care Hospital from July 2016 to October 2017. A series of validated land-use regression (LUR) models were built to assess individual exposure to PM_2.5 in a 1 × 1 km area at both work and home addresses following a time-weighted pattern. Multiple linear regression and logistic regression analyses were performed to examine the association between PM_2.5 exposure and GDM and fasting glucose.

RESULTS

In total, 11,639 of 16,995 women were included in the final analysis. Among the 11,639 women, 2776 (23.85%) had GDM. Individual exposure to PM_2.5 within three months before pregnancy ranged from 21.58 to 85.92 μg/m³. Positive associations were observed among the interquartile ranges (IQRs) of exposure to PM_2.5 within three months before pregnancy and GDM (OR = 2.61, 95% CI: 1.40-4.93, p < .01) as well as fasting glucose levels (β = 0.57, 95% CI: 0.45-0.68, p < .01). The diabetogenic effects of PM_2.5 gradually increased from the first month before pregnancy, peaked in the second month and then gradually decreased until the third month when the week-specific exposure were analyzed to identify the sensitive time window.

CONCLUSION

Our study confirmed that higher exposure to PM_2.5 within three months before pregnancy is significantly associated with increased risk of GDM and elevated fasting glucose levels, reflecting the importance of preconceptional environmental exposure in the development of maternal GDM.

Epicardial adipose tissue thickness as a potential predictor of gestational diabetes mellitus: a prospective cohort study

BACKGROUND

Gestational diabetes mellitus (GDM) is the most common metabolic disorder that can occur during pregnancy and is associated with a long-term risk of both maternal and neonatal comorbidities. This study aimed to investigate the association between echocardiographic epicardial adipose tissue (EAT) and the risk for GDM during the early second trimester of pregnancy.

METHOD

We recruited all singleton pregnancies between January 2014 and December 2018 at 16 weeks + 0 days to 19 weeks + 6 days. We then used generalized linear models to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for EAT as a potential predictor for GDM. Receiver-operating-characteristic (ROC) analysis was then conducted to investigate the discriminative capacity of any individual maternal factor for the prediction of GDM.

RESULTS

In total, our study involved 314 pregnant women with GDM and 1832 pregnant women without GDM. Multivariate regression analysis revealed that EAT thickness (OR = 2.87; 95% CI: 2.49-3.31) was significantly associated with the presence of GDM (P < 0.001). Furthermore, EAT thickness was also significantly associated with a range of adverse outcomes in the GDM group, including large size for gestational age, neonatal hypoglycemia, admission to the neonatal intensive care unit, preterm delivery, and hyperbilirubinemia (P < 0.001). ROC analysis revealed that the area under the curve was 0.790 (95% CI: 0.768-0.812). When the cutoff value for EAT thickness was set to 5.49 mm, the sensitivity was 95.2% and the specificity was 50.5%.

CONCLUSIONS

Echocardiographic EAT thickness is positively and significantly associated with both the risk of GDM and adverse outcomes related to GDM. Echocardiographic EAT has the potential to predict GDM prior to actual clinical diagnosis.