First trimester biomarkers for prediction of gestational diabetes mellitus

Early prediction of gestational diabetes mellitus using first trimester maternal serum pregnancy-associated plasma protein-a: A cross-sectional study

Background and Aims

The oral glucose tolerance test with 75 g glucose is commonly regarded as the gold standard (GS) for the detection of gestational diabetes mellitus (GDM). However, one limitation of this test is its administration in the late second trimester of pregnancy in some countries (e.g., Iran). This study aimed to evaluate the accuracy of pregnancy-associated plasma protein-A (PAPP-A) for predicting GDM in the early first trimester of pregnancy using a novel statistical modeling technique.

Methods

The study population consisted of 344 pregnant women who participated in the first trimester screening program for GDM. Maternal serum PAPP-A levels were measured between 11 and 13 gestational weeks for all participants. A Bayesian latent profile model (LPM) under the skew-t (ST) distribution was employed to estimate the diagnostic accuracy measures of PAPP-A in the absence of GS test outcomes.

Results

The mean (standard deviation) age of the participants was 28.87 ± 5.20 years. The median (interquartile range (IQR)) PAPP-A MoM was 0.91 (0.69-1.34). Utilizing the LPM under the ST distribution while adjusting for covariates, the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of PAPP-A were 92% (95% credible interval [CrI]: 0.89, 0.98), 81% (95% CrI: 0.76, 0.91), and 0.91 (95% CrI: 0.83, 0.97), respectively. Notably, the pregnant women with GDM had significantly lower PAPP-A values (β = -0.52, 95% CrI [-0.61, -0.46]).

Conclusion

Generally, our findings confirmed that PAPP-A could serve as a potential screening tool for the identification of GDM in the early stages of pregnancy.

Preliminary Investigation of Potential Early Biomarkers for Gestational Diabetes Mellitus: Insights from PTRPG and IGKV2D-28 Expression Analysis

Gestational diabetes mellitus (GDM) poses significant health risks to both mothers and infants, emphasizing the need for early detection strategies to mitigate its impact. However, the existing diagnostic methods, particularly the oral glucose tolerance test (OGTT) administered in the second or third trimester, show limitations in the detection of GDM during its early stages. This study aimed to explore the potential of the genes Protein Tyrosine Phosphatase Receptor-type Gamma (PTPRG) and Immunoglobulin Kappa Variable 2D-28 (IGKV2D-28) as early indicators for GDM among Filipino pregnant women. Utilizing reverse transcription-quantitative polymerase chain reaction (RT-qPCR), the gene expressions were analyzed in first-trimester blood samples obtained from 24 GDM and 36 non-GDM patients. The diagnostic performance of PTPRG and IGKV2D-28 was analyzed and evaluated using receiver operating characteristic (ROC) curves. The findings revealed elevated expression levels of PTPRG and IGKV2D-28 within the GDM cohort. Remarkably, PTPRG exhibited a sensitivity of 83%, while IGKV2D-28 demonstrated a specificity of 94% at determined cut-off values. Combining both genes yielded an improved but limited diagnostic accuracy with an area under the curve (AUC) of 0.63. This preliminary investigation of PTPRG and IGKV2D-28 sheds light on novel avenues for early GDM detection. While these findings are promising, further validation studies in larger cohorts are necessary to confirm these results and explore additional biomarkers to enhance diagnostic precision in GDM pregnancies and, ultimately, to improve maternal and fetal outcomes.

First trimester maternal serum PAPP-A and free β-hCG levels and risk of SGA or LGA in women with and without GDM

BACKGROUND

Maternal gestational diabetes (GDM), small (SGA) and large (LGA) for gestational age neonates are associated with increased morbidity in both mother and child. We studied how different levels of first trimester pregnancy associated plasma protein-A (PAPP-A) and free beta human chorionic gonadotropin (fβ-hCG) were associated with SGA and LGA in GDM pregnancies and controls.

METHODS

Altogether 23 482 women with singleton pregnancies participated in first trimester combined screening and delivered between 2014 and 2018 in Northern Finland and were included in this retrospective case-control study. Women with GDM (n = 4697) and controls without GDM (n = 18 492) were divided into groups below 5th and 10th or above 90th and 95th percentile (pc) PAPP-A and fβ-hCG MoM levels. SGA was defined as a birthweight more than two standard deviations (SD) below and LGA more than two SDs above the sex-specific and gestational age-specific reference mean. Odds ratios were adjusted (aOR) for maternal age, BMI, ethnicity, IVF/ICSI, parity and smoking.

RESULTS

In pregnancies with GDM the proportion of SGA was 2.6% and LGA 4.5%, compared to 3.3% (p = 0.011) and 1.8% (p < 0.001) in the control group, respectively. In ≤ 5th and ≤ 10th pc PAPP-A groups, aORs for SGA were 2.7 (95% CI 1.5-4.7) and 2.2 (95% CI 1.4-3.5) in the GDM group and 3.8 (95% CI 3.0-4.9) and 2.8 (95% CI 2.3-3.5) in the reference group, respectively. When considering LGA, there was no difference in aORs in any high PAPP-A groups. In the low ≤ 5 percentile fβ-hCG MoM group, aORs for SGA was 2.3 (95% CI 1.8-3.1) in the control group. In fβ-hCG groups with GDM there was no association with SGA and the only significant difference was ≥ 90 percentile group, aOR 1.6 (95% CI 1.1-2.5) for LGA.

CONCLUSION

Association with low PAPP-A and SGA seems to be present despite GDM status. High PAPP-A levels are not associated with increased LGA risk in women with or without GDM. Low fβ-hCG levels are associated with SGA only in non-GDM pregnancies.

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.

Mid-upper arm circumference predicts the risk of gestational diabetes in early pregnancy

BACKGROUND

The present work aimed to assess the value of mid-upper arm circumference (MUAC) at 8 to 12 weeks in predicting the occurrence of gestational diabetes mellitus (GDM).

METHODS

According to eligibility criteria, 328 women with singleton pregnancies who underwent routine antenatal check-ups at Qinhuangdao Maternal and Child Health Hospital from September 2017 to September 2020 were included. The patients were divided into the gestational diabetes mellitus (GDM) and non-GDM groups according to oral glucose tolerance test (OGTT) data from gestation weeks 24 to 28. Clinical data were compared between the two groups. Logistic regression analysis was performed to determine factors independently predicting GDM. Receiver operating characteristic (ROC) curve analysis was employed to analyze the value of MUAC in predicting the occurrence of GDM. The optimal cut-off points were calculated.

RESULTS

In logistic regression analysis, pre-pregnancy weight, waist circumference, MUAC, UA, TG, and HDL-C independently predicted the occurrence of GDM (P < 0.05). MUAC retained statistical significance upon adjustment for various confounders (OR = 8.851, 95%CI: 3.907-20.048; P < 0.001). ROC curve analysis revealed good diagnostic potential for MUAC in GDM (AUC = 0.742, 95%CI: 0.684-0.800, P < 0.001), with a cut-off of 28.5 cm, sensitivity and specificity were 61% and 77%, respectively.

CONCLUSION

Pregnant women with MUAC >28.5 cm are prone to develop GDM during pregnancy, indicating that MUAC as an important predictive factor of GDM in early pregnancy.

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.

Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors

OBJECTIVE

To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.

METHODS

To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.

RESULTS

An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).

The association between interleukin family and diabetes mellitus and its complications: An overview of systematic reviews and meta-analyses

OBJECTIVE

To evaluate and summarize the association between interleukin (IL) concentrations and diabetes mellitus (DM) and its complications.

METHODS

Meta-analyses and eligible individual studies of observational studies investigating the associations between IL and DM and its complications were included. The random-effects model was used to estimate the summary effect, and the heterogeneity among studies was assessed using the Q-statistic and the I2 metric; The Egger's regression and the χ2 test were used to test for small study effects and excess significance bias.

RESULTS

This overview identified 34 meta-analyses that investigated the association between IL concentrations and DM and its complications. Meta-analyses of prospective studies indicated that elevated circulating IL-6 and IL-1β had predictive value for the incident of type 2 diabetes mellitus (T2DM), type 1 diabetes mellitus (T1DM) as well as gestational diabetes mellitus (GDM), and the overall Hazard Ratio (HR) of T2DM was 1.28 (95 % CI: 1.17, 1.40; P＜0.001) per 1 log pg/ml increment in IL-6 levels, however, there was no correlation between circulating IL-10 levels and DM. Meanwhile, the increased level of IL-6 was significantly associated several diabetic complications (Diabetic kidney disease[DKD], diabetic peripheral neuropathy[DPN], and cognitive impairment[CI]), and for the diabetic retinopathy (DR), the levels of IL-1β, IL-8 and IL-10 in the aqueous humor and vitreous humor, but not the blood were significantly correlated with it.

CONCLUSION

Multiple ILs, such as the IL-6 and IL-1β, are definitively linked to DM and its complications, and they may be new targets for the diagnosis and treatment, but stronger evidence needs to be confirmed by prospective studies with larger sample sizes and longer observation periods.

The maternal gut microbiome in pregnancy: implications for the developing immune system

The gut microbiome has important roles in host metabolism and immunity, and microbial dysbiosis affects human physiology and health. Maternal immunity and microbial metabolites during pregnancy, microbial transfer during birth, and transfer of immune factors, microorganisms and metabolites via breastfeeding provide critical sources of early-life microbial and immune training, with important consequences for human health. Only a few studies have directly examined the interactions between the gut microbiome and the immune system during pregnancy, and the subsequent effect on offspring development. In this Review, we aim to describe how the maternal microbiome shapes overall pregnancy-associated maternal, fetal and early neonatal immune systems, focusing on the existing evidence and highlighting current gaps to promote further research.

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

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

Doppler Indices of the Uterine, Umbilical and Fetal Middle Cerebral Artery in Diabetic versus Non-Diabetic Pregnancy: Systematic Review and Meta-Analysis

Background and Objectives: The aim of this study was to assess the differences in Doppler indices of the uterine (Ut), umbilical (UA), and middle cerebral artery (MCA) in diabetic versus non-diabetic pregnancies by conducting a comprehensive systematic review of the literature with a meta-analysis. Materials and Methods: PubMed, Web of Science, and SCOPUS were searched for studies that measured the pulsatility index (PI), resistance index (RI), and systolic/diastolic ratio index (S/D ratio) of the umbilical artery, middle cerebral artery, and uterine artery in diabetic versus non-diabetic pregnancies. Two reviewers independently evaluated the eligibility of studies, abstracted data, and performed quality assessments according to standardized protocols. The standardized mean difference (SMD) was used as a measure of effect size. Heterogeneity was assessed using the I2 statistic. Publication bias was evaluated by means of funnel plots. Results: A total of 62 publications were included in the qualitative and 43 in quantitative analysis. The UA-RI, UtA-PI, and UtA-S/D ratios were increased in diabetic compared with non-diabetic pregnancies. Subgroup analysis showed that levels of UtA-PI were significantly higher during the third, but not during the first trimester of pregnancy in diabetic versus non-diabetic pregnancies. No differences were found for the UA-PI, UA-S/D ratio, MCA-PI, MCA-RI, MCA-S/D ratio, or UtA-RI between diabetic and non-diabetic pregnancies. Conclusions: This meta-analysis revealed the presence of hemodynamic changes in uterine and umbilical arteries, but not in the middle cerebral artery in pregnancies complicated by diabetes.

Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis

OBJECTIVE

Gestational diabetes mellitus (GDM) is a condition in which women without diabetes are diagnosed with glucose intolerance during pregnancy, typically in the second or third trimester. Early diagnosis, along with a better understanding of its pathophysiology during the first trimester of pregnancy, may be effective in reducing incidence and associated short-term and long-term morbidities.

DESIGN

We comprehensively profiled the gut microbiome, metabolome, inflammatory cytokines, nutrition and clinical records of 394 women during the first trimester of pregnancy, before GDM diagnosis. We then built a model that can predict GDM onset weeks before it is typically diagnosed. Further, we demonstrated the role of the microbiome in disease using faecal microbiota transplant (FMT) of first trimester samples from pregnant women across three unique cohorts.

RESULTS

We found elevated levels of proinflammatory cytokines in women who later developed GDM, decreased faecal short-chain fatty acids and altered microbiome. We next confirmed that differences in GDM-associated microbial composition during the first trimester drove inflammation and insulin resistance more than 10 weeks prior to GDM diagnosis using FMT experiments. Following these observations, we used a machine learning approach to predict GDM based on first trimester clinical, microbial and inflammatory markers with high accuracy.

CONCLUSION

GDM onset can be identified in the first trimester of pregnancy, earlier than currently accepted. Furthermore, the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis. Potential GDM markers, including microbiota, can serve as targets for early diagnostics and therapeutic intervention leading to prevention.

Circulating lncRNAs NONHSAT054669.2 and ENST00000525337 can be used as early biomarkers of gestational diabetes mellitus

Early diagnosis can help prevent and reduce the adverse effects of gestational diabetes mellitus (GDM). This study intended to investigate key circulating long non-coding RNAs (lncRNAs) as novel biomarkers for diagnosis of GDM at the early stages. First, lncRNA microarray analysis was conducted for plasma samples of GDM women before delivery and 48 h after delivery. The expression of differentially expressed lncRNAs in clinical samples at different trimesters was randomly validated by quantitative polymerase chain reaction (PCR). Moreover, the correlation between lncRNA expression and oral glucose tolerance test (OGTT) level in GDM women during the second trimester was analyzed, followed by evaluating the diagnostic value of key lncRNAs during different trimesters using receiver operating characteristic (ROC) curve. Higher NONHSAT054669.2 expression and lower ENST00000525337 expression were revealed in GDM women before delivery relative to 48 h after delivery (P < 0.05). The expression of NONHSAT054669.2 and ENST00000525337 in GDM women during the first and second trimesters was dramatically higher than pregnant women (P < 0.05) with normal glucose tolerance (NGT). During the second trimester, NONHSAT054669.2 expression was positively related to OGTT level at 1 h (r = 0.41455, P < 0.001). Furthermore, ROC curve analysis revealed that ENST00000525337 alone, NONHSAT054669.2 alone, and their combination had high diagnostic value for GDM during the first (area under the ROC curve (AUC) = 0.979, 0.956, and 0.984, respectively) and second (AUC = 0.829, 0.809, and 0.838, respectively) trimesters (all P < 0.001). The plasma level of NONHSAT054669.2 and ENST00000525337 may be applied as novel diagnostic biomarkers for early diagnosis of GDM.

The role of ultrasonographic adipose tissue thickness measurement in the first trimester in predicting gestational diabetes: a prospective study

BACKGROUND

This prospective observational study aimed to assess the association between maternal abdominal subcutaneous and visceral fat thickness measured with ultrasound scan during the first trimester and the risk of developing gestational diabetes mellitus (GDM).

METHODS

We recruited 43 non-diabetic women with singleton pregnancy between 11 and 14 week of gestation and evaluated ultrasonographic measurements of subcutaneous fat thickness (SFT) and preperitoneal fat (PF) above the umbilicus. During the 2^nd trimester, GDM screening was performed by 75 g two-hour oral glucose tolerance test (OGTT) and diagnosis was made when one or more plasma glucose values meets or exceeds the values indicated by International Association of the Diabetes and Pregnancy Study Groups (IADPSG).

RESULTS

Among the 43 woman, 8 developed GDM (18.6%). Of these 37,5% (N.=3) had been diagnosed with GDM during a previous pregnancy, with a statistically significant correlation (P=0.035). Mean SFT for all patients was significantly higher in the GDM group compared to non-GDM group (27.30±8.78 mm vs. 18.56±9.99 mm; P=0.049). Mean PF for all women showed a statistically significant correlation with GDM (13.27±9.07 mm for non GDM group vs. 23.52±10.24 mm for GDM group; P=0.038).

CONCLUSIONS

Abdominal adiposity, both subcutaneous and visceral, seem to be a suitable predictor of GDM in early pregnancy and it can be easily assessed during a first trimester routine ultrasound, although further studies are needed to evaluate their role in the screening protocols.

Multi-omics and machine learning for the prevention and management of female reproductive health

Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Risk prediction models of gestational diabetes mellitus before 16 gestational weeks

BACKGROUND

Gestational diabetes mellitus (GDM) can lead to adverse maternal and fetal outcomes, and early prevention is particularly important for their health, but there is no widely accepted approach to predict it in the early pregnancy. The aim of the present study is to build and evaluate predictive models for GDM using routine indexes, including maternal clinical characteristics and laboratory biomarkers, before 16 gestational weeks.

METHODS

A total of 2895 pregnant women were recruited and maternal clinical characteristics and laboratory biomarkers before 16 weeks of gestation were collected from two hospitals. All participants were randomly stratified into the training cohort and the internal validation cohort by the ratio of 7:3. Using multivariable logistic regression analysis, two nomogram models, including a basic model and an extended model, were built. The discrimination, calibration, and clinical validity were used to evaluate the models in the internal validation cohort.

RESULTS

The area under the receiver operating characteristic curve of the basic and the extended model was 0.736 and 0.756 in the training cohort, and was 0.736 and 0.763 in the validation cohort, respectively. The calibration curve analysis showed that the predicted values of the two models were not significantly different from the actual observations (p = 0.289 and 0.636 in the training cohort, p = 0.684 and 0.635 in the internal validation cohort, respectively). The decision-curve analysis showed a good clinical application value of the models.

CONCLUSIONS

The present study built simple and effective models, indicating that routine clinical and laboratory parameters can be used to predict the risk of GDM in the early pregnancy, and providing a novel reference for studying the prediction of GDM.

HtrA1 in Gestational Diabetes Mellitus: A Possible Biomarker?

BACKGROUND

The high-temperature requirement A 1 (HtrA1) is a multidomain secretory protein with serine-protease activity, expressed in many tissues, including placenta, where its expression is higher in the first trimester, suggesting an association of this serine protease in early phases of human placenta development. In this study, we evaluated maternal serum HtrA1 levels in the first and third trimester of gestation. In particular, we evaluated a possible role of HtrA1 as an early marker of gestational diabetes mellitus (GDM) in the first trimester of gestation.

METHODS

We evaluated HtrA1 serum levels in the third trimester (36-40 weeks) in normal pregnancies (n = 20) and GDM pregnancies (n = 20) by using ELISA analysis. Secondly, we performed the same analysis by using the first trimester sera (10-12 weeks) of healthy pregnant women that will develop a normal pregnancy (n = 210) or GDM (n = 28) during pregnancy.

RESULTS

We found that HtrA1 serum levels in the third trimester were higher in pregnancies complicated by GDM. Interestingly, higher HtrA1 serum levels were also found in the first trimester in women developing GDM later during the second-third trimester. No significant differences in terms of maternal age and gestational age were found between cases and controls. Women with GDM shown significantly higher pre-pregnancy BMI values compared to controls. Moreover, the probability of GDM occurrence significantly increased with increasing HtrA1 levels and BMI values. The ROC curve showed a good accuracy in predicting GDM, with an AUC of 0.74 (95%CI: 0.64-0.92).

CONCLUSIONS

These results suggest an important role of HtrA1 as an early predictive marker of GDM in the first trimester of gestation, showing a significative clinical relevance for prevention of this disease.

Value of PAPP-A combined with BMI in predicting the prognosis of gestational diabetes mellitus: an observational study

The aim of this study was to investigate the potential of pregnancy-associated plasma protein A (PAPP-A) and clinical data in predicting gestational diabetes mellitus (GDM). Clinical data of 318 pregnant women with GDM and 200 healthy pregnant women were retrospectively analysed. The age, BMI and caesarean section in GDM were significantly higher than in normal group. Serum and placental levels of PAPP-A were significantly lower in GDM than in normal group. Pearson's correlation analysis showed that serum levels of PAPP-A were negatively correlated with BMI and blood glucose level. Binary logistic regression analysis displayed that PAPP-A were the potential factors influencing GDM. The area under the ROC curve (AUC) for PAPP-A combined with BMI in predicting GDM was 0.941, significantly higher than that of the single one. The potential of PAPP-A in the first trimester is limited in predicting GDM. PAPP-A combined with BMI is highly conductive for predicting GDM.Impact statementWhat is already known on this subject? GDM not only increases the risk of perinatal morbidity, but also results in an increased risk of long-term sequelae for both mother and child including diabetes, cardiovascular disease obesity. Previous data indicate that besides glycemic control in the second trimester, interventions initiated early in pregnancy can reduce the rate of GDM in pregnant women. The expression of PAPP-A in serum of GDM pregnant women was decreased in the first trimester. Whereas, whether PAPP-A can be as an early predictor of GDM is not clear.What do the results of this study add? The present study shows that PAPP-A MoM was less than 0.6757 in the first trimester of pregnancy is more prone to GDM. The potential of PAPP-A in the first trimester is limited in predicting GDM. PAPP-A combined with BMI is highly conductive for predicting GDM.What are the implications of these findings for clinical practice and/or further research? Early GDM prediction is crucial for prevention and management of GDM, to cope with the rising prevalence of GDM and reduce later life chronic disease of both mother and child. Based on the level of PAPP-A MoM and BMI, interventions such as lifestyle changes initiated early in pregnancy shouldbeenabledin pregnant women.

Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications

We assessed the diagnostic potential of cardiovascular disease-associated microRNAs for the early prediction of gestational diabetes mellitus (GDM) in singleton pregnancies of Caucasian descent in the absence of other pregnancy-related complications. Whole peripheral venous blood samples were collected within 10 to 13 weeks of gestation. This retrospective study involved all pregnancies diagnosed with only GDM (n = 121) and 80 normal term pregnancies selected with regard to equality of sample storage time. Gene expression of 29 microRNAs was assessed using real-time RT-PCR. Upregulation of 11 microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-181a-5p, miR-195-5p, miR-499a-5p, and miR-574-3p) was observed in pregnancies destinated to develop GDM. Combined screening of all 11 dysregulated microRNAs showed the highest accuracy for the early identification of pregnancies destinated to develop GDM. This screening identified 47.93% of GDM pregnancies at a 10.0% false positive rate (FPR). The predictive model for GDM based on aberrant microRNA expression profile was further improved via the implementation of clinical characteristics (maternal age and BMI at early stages of gestation and an infertility treatment by assisted reproductive technology). Following this, 69.17% of GDM pregnancies were identified at a 10.0% FPR. The effective prediction model specifically for severe GDM requiring administration of therapy involved using a combination of these three clinical characteristics and three microRNA biomarkers (miR-20a-5p, miR-20b-5p, and miR-195-5p). This model identified 78.95% of cases at a 10.0% FPR. The effective prediction model for GDM managed by diet only required the involvement of these three clinical characteristics and eight microRNA biomarkers (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, and miR-574-3p). With this, the model identified 50.50% of GDM pregnancies managed by diet only at a 10.0% FPR. When other clinical variables such as history of miscarriage, the presence of trombophilic gene mutations, positive first-trimester screening for preeclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm, and family history of diabetes mellitus in first-degree relatives were included in the GDM prediction model, the predictive power was further increased at a 10.0% FPR (72.50% GDM in total, 89.47% GDM requiring therapy, and 56.44% GDM managed by diet only). Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for GDM.

Maternal metabolic factors and the association with gestational diabetes: A systematic review and meta-analysis

Gestational diabetes (GDM) is associated with several adverse outcomes for the mother and child. Higher levels of individual lipids are associated with risk of GDM and metabolic syndrome (MetS), a clustering of risk factors also increases risk for GDM. Metabolic factors can be modified by diet and lifestyle. This review comprehensively evaluates the association between MetS and its components, measured in early pregnancy, and risk for GDM. Databases (Cumulative Index to Nursing and Allied Health Literature, PubMed, Embase, and Cochrane Library) were searched from inception to 5 May 2021. Eligible studies included ≥1 metabolic factor (waist circumference, blood pressure, fasting plasma glucose (FPG), triglycerides, and high-density lipoprotein cholesterol), measured at <16 weeks' gestation. At least two authors independently screened potentially eligible studies. Heterogeneity was quantified using I² . Data were pooled by random-effects models and expressed as odds ratio and 95% confidence intervals (CIs). Of 7213 articles identified, 40 unique articles were included in meta-analysis. In analyses adjusting for maternal age and body mass index, GDM was increased with increasing FPG (odds ratios [OR] 1.92; 95% CI 1.39-2.64, k = 7 studies) or having MetS (OR 2.52; 1.65, 3.84, k = 3). Women with overweight (OR 2.17; 95% CI 1.89, 2.50, k = 12) or obesity (OR 4.34; 95% CI 2.79-6.74, k = 9) also were at increased risk for GDM. Early pregnancy assessment of glucose or the MetS, offers a potential opportunity to detect and treat individual risk factors as an approach towards GDM prevention; weight loss for pregnant women with overweight or obesity is not recommended. Systematic review registration: PROSPERO CRD42020199225.

Maternal Nutritional Status and Pregnancy Outcomes Post-bariatric Surgery

Obesity in childbearing women leads to pregnancy-related complications such as gestational diabetes mellitus, pregnancy-associated hypertensive disorders, and macrosomia. Weight loss helps reduce these complications. Studies show bariatric surgery reduces obesity-related complications during and after pregnancy. However, bariatric surgery might be associated with adverse outcomes, such as low birth weight and small-for-gestational-age infants. In addition, several studies suggest pregnancy occurring less than a year post-bariatric surgery adversely affects pregnancy outcomes and causes micronutrients deficiency since the dramatic weight loss occurs in the first year. These adverse outcomes may lead to nutritional malabsorption, such as anemia and low vitamin B12 and folic acid levels. The review aims to overview obesity-related complications during pregnancy and the benefits and risks of bariatric surgery on pregnancy outcomes and maternal nutrition status.

The P4 Study: Postpartum Maternal and Infant Faecal Microbiome 6 Months After Hypertensive Versus Normotensive Pregnancy

Objective/Hypothesis

To explore potential differences in faecal microbiome between women, and their infants, who had normotensive pregnancies (NP) and those who had a hypertensive pregnancy (HP), either gestational hypertension (GH) or preeclampsia (PE).

Methods

This is a sub study of P4 (Postpartum Physiology, Psychology, and Paediatrics Study) and includes 18 mother-infant pairs: 10 NP and 8 HP (HP as defined by blood pressure > 140/90mmHg; of which 6 had PE, and 2 GH), six months postpartum. The participating mothers collected stool samples from themselves and their infants. 16S rRNA V3-V4 amplicons were used to study the faecal microbiome.

Results

The sample of women and their infants were mostly primiparous (n =16) with vaginal birth (n = 14). At the time of faecal sampling 8 women were using hormonal contraception, and one HP woman remained on an antihypertensive. All women had blood pressure < 130/80mmHg, and 10 had high BMI (> 30). All infants had started solids, 8 were exclusively breastfed, 1 exclusively formula fed and 9 both. Three infants had been exposed to a course of antibiotics. Six months postpartum, there were no significant differences in alpha or beta diversity between the gut microbiota of HP and NP women (P > 0.05). However, a statistically significant difference was detected in alpha diversity between infants following HP and NP, with lower diversity levels in HP infants (P < 0.05). It was also found that at a genus and species level, the gut microbiota of HP women was enriched with Bifidobacterium and Bifidobacterium sp. and depleted in Barnesiella and Barnesiella intestinihominis when compared to NP women (P < 0.05). Similarly, the gut microbiota of infants born from HP was enriched in Streptococcus infantis and depleted in Sutterella, Sutterella sp., Bacteroides sp. and Clostridium aldenense compared to infants born from NP (P < 0.05).

Discussion

While our findings are at best preliminary, due to the very small sample size, they do suggest that the presence of hypertension in pregnancy may adversely affect the maternal microbiota postpartum, and that of their infants. Further analysis of postpartum microbiome data from future studies will be important to validate these early findings and provide further evidence about the changes in the microbiota in the offspring of women following hypertensive disorders of pregnancy (HDP), including possible links to the causes of long-term cardiovascular disease, the prevalence of which is increased in women who have experienced HDP.

Diagnostic accuracy of first and early second trimester multiple biomarkers for prediction of gestational diabetes mellitus: a multivariate longitudinal approach

BACKGROUND

Gestational Diabetes Mellitus (GDM) is an underlying cause of maternal and newborn morbidity and mortality all around the world. Timely diagnosis of GDM plays an important role in reducing its adverse consequences and burden. This study aimed to determine diagnostic accuracy of multiple indicators in complete blood count (CBC) test for early prediction of GDM.

METHODS

In this prospective cohort study, the data from 600 pregnant women was analyzed. In the study sample, the two-step approach was utilized for the diagnosis of GDM at 24-28 weeks of gestation. We also used the repeated measures of hemoglobin (Hb), hematocrit (Hct), fasting blood sugar (FBS) and red blood cell count (RBC) in the first and early second trimesters of pregnancy as the longitudinal multiple indicators for early diagnosis of GDM. The classification of pregnant women to GDM and non-GDM groups was performed using a statistical technique based on the random-effects modeling framework.

RESULTS

Among the sample, 49 women (8.2%) were diagnosed with GDM. In the first and early second trimester of pregnancy, the mean HcT, Hb and FBS of women with GDM was significantly higher than non-GDMs (P < 0.001). The concurrent use of multiple longitudinal data from HcT, Hb, RBC and FBS in the first and early second trimester of pregnancy resulted in a sensitivity, specificity and area under the curve (AUC) of 87%, 70% and 83%, respectively, for early prediction of GDM.

CONCLUSIONS

In general, our findings showed that the concurrent use of repeated measures data on Hct, Hb, FBS and RBC in the first and early second trimester of pregnancy might be utilized as an acceptable tool to predict GDM earlier in pregnancy.

Gas Chromatography-Mass Spectroscopy-Based Metabolomics Analysis Reveals Potential Biochemical Markers for Diagnosis of Gestational Diabetes Mellitus

Due to many adverse effects of gestational diabetes mellitus (GDM) on the mother and fetus, its diagnosis is crucial. The presence of GDM can be confirmed by an abnormal fasting plasma glucose level (aFPG) and/or oral glucose tolerance test (OGTT) performed mostly between 24 and 28 gestational week. Both aFPG and abnormal glucose tolerance (aGT) are used to diagnose GDM. In comparison to measurement of FPG, OGTT is time-consuming, usually inconvenient for the patient, and very often needs to be repeated. Therefore, it is necessary to seek tests that will be helpful and convenient to diagnose GDM. For this reason, we investigated the differences in fasting serum metabolites between GDM women with abnGM and normal FPG (aGT-GDM group), with aFPG and normal glucose metabolism (aFPG-GDM group) as well as pregnant women with normal glucose tolerance (NGT) being a control group. Serum metabolites were measured by an untargeted approach using gas chromatography–mass spectrometry (GC–MS). In the discovery phase, fasting serum samples collected from 79 pregnant women (aFPG-GDM, n = 24; aGT-GDM, n = 26; NGT, n = 29) between 24 and 28 weeks of gestation (gwk) were fingerprinted. A set of metabolites (α–hydroxybutyric acid (α–HB), β–hydroxybutyric acid (β–HB), and several fatty acids) significant in aGT-GDM vs NGT but not significant in aFPG-GDM vs NGT comparison in the discovery phase was selected for validation. These metabolites were quantified by a targeted GC–MS method in a validation cohort consisted of 163 pregnant women (aFPG-GDM, n = 51; aGT-GDM, n = 44; and NGT, n = 68). Targeted analyses were also performed on the serum collected from 92 healthy women in the first trimester (8–14 gwk) who were NGT at this time, but in the second trimester (24–28 gwk) they were diagnosed with GDM. It was found that α–HB, β–HB, and several fatty acids were associated with aGT-GDM. A combination of α–HB, β–HB, and myristic acid was found highly specific and sensitive for the diagnosis of GDM manifested by aGT-GDM (AUC = 0.828) or to select women at a risk of aGT-GDM in the first trimester (AUC = 0.791). Our findings provide new potential markers of GDM and may have implications for its early diagnosis.