Fasting plasma glucose in the first trimester is related to gestational diabetes mellitus and adverse pregnancy outcomes

Prediction of gestational diabetes mellitus by multiple biomarkers at early gestation

BACKGROUND

It remains unclear which early gestational biomarkers can be used in predicting later development of gestational diabetes mellitus (GDM). We sought to identify the optimal combination of early gestational biomarkers in predicting GDM in machine learning (ML) models.

METHODS

This was a nested case-control study including 100 pairs of GDM and euglycemic (control) pregnancies in the Early Life Plan cohort in Shanghai, China. High sensitivity C reactive protein, sex hormone binding globulin, insulin-like growth factor I, IGF binding protein 2 (IGFBP-2), total and high molecular weight adiponectin and glycosylated fibronectin concentrations were measured in serum samples at 11-14 weeks of gestation. Routine first-trimester blood test biomarkers included fasting plasma glucose (FPG), serum lipids and thyroid hormones. Five ML models [stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, support vector machine and k-nearest neighbor] were employed to predict GDM. The study subjects were randomly split into two sets for model development (training set, n = 70 GDM/control pairs) and validation (testing set: n = 30 GDM/control pairs). Model performance was evaluated by the area under the curve (AUC) in receiver operating characteristics.

RESULTS

FPG and IGFBP-2 were consistently selected as predictors of GDM in all ML models. The random forest model including FPG and IGFBP-2 performed the best (AUC 0.80, accuracy 0.72, sensitivity 0.87, specificity 0.57). Adding more predictors did not improve the discriminant power.

CONCLUSION

The combination of FPG and IGFBP-2 at early gestation (11-14 weeks) could predict later development of GDM with moderate discriminant power. Further validation studies are warranted to assess the utility of this simple combination model in other independent cohorts.

Serum progesterone, glycosylated hemoglobin and insulin levels with the risk of premature rupture of membranes in gestational diabetes mellitus

OBJECTIVE

To discuss the correlation between serum progesterone, glycosylated Hemoglobin (HbA1c), and insulin levels in pregnant women with Gestational Diabetes Mellitus (GDM) and the risk of Premature Rupture of Membranes (PROM).

METHODS

A retrospective analysis was conducted on 52 patients diagnosed with GDM who also presented with PROM (Observation group) and compared with 89 patients diagnosed with GDM but not complicated with PROM (Control group). Progesterone, insulin, and HbA1c were detected. Risk factors for PROM in GDM patients were analyzed.

RESULTS

The observation group had higher HbA1c and fasting blood glucose levels. Poor blood glucose control and GWG are risk factors for PROM in GDM patients. PROM increases adverse pregnancy outcomes in GDM. HbA1c, insulin, and HOMA-IR can predict the risk of PROM in GDM.

CONCLUSIONS

The effective prediction of preterm PROM can be achieved through the monitoring of serum HbA1c, insulin levels, and insulin resistance in patients with GDM.

Functional genetic variants and susceptibility and prediction of gestational diabetes mellitus

The aetiological mechanism of gestational diabetes mellitus (GDM) has still not been fully understood. The aim of this study was to explore the associations between functional genetic variants screened from a genome-wide association study (GWAS) and GDM risk among 554 GDM patients and 641 healthy controls in China. Functional analysis of single nucleotide polymorphisms (SNPs) positively associated with GDM was further performed. Univariate regression and multivariate logistic regression analyses were used to screen clinical risk factors, and a predictive nomogram model was established. After adjusting for age and prepregnancy BMI, rs9283638 was significantly associated with GDM susceptibility (P < 0.05). Moreover, an obvious interaction between rs9283638 and clinical variables was detected (Pinteraction < 0.05). Functional analysis confirmed that rs9283638 can regulate not only target gene transcription factor binding, but it also regulates the mRNA levels of SAMD7 (P < 0.05). The nomogram model constructed with the factors of age, FPG, 1hPG, 2hPG, HbA1c, TG and rs9283638 revealed an area under the ROC curve of 0.920 (95% CI 0.902-0.939). Decision curve analysis (DCA) suggested that the model had greater net clinical benefit. Conclusively, genetic variants can alter women's susceptibility to GDM by affecting the transcription of target genes. The predictive nomogram model constructed based on genetic and clinical variables can effectively distinguish individuals with different GDM risk factors.

A stepwise prediction and interpretation of gestational diabetes mellitus: Foster the practical application of machine learning in clinical decision

Background

Machine learning has shown to be an effective method for early prediction and intervention of Gestational diabetes mellitus (GDM), which greatly decreases GDM incidence, reduces maternal and infant complications and improves the prognosis. However, there is still much room for improvement in data quality, feature dimension, and accuracy. The contributions and mechanism explanations of clinical data at different pregnancy stages to the prediction accuracy are still lacking. More importantly, current models still face notable obstacles in practical applications due to the complex and diverse input features and difficulties in redeployment. As a result, a simple, practical but accurate enough model is urgently needed.

Design and methods

In this study, 2309 samples from two public hospitals in Shenzhen, China were collected for analysis. Different algorithms were systematically compared to build a robust and stepwise prediction system (level A to C) based on advanced machine learning, and models under different levels were interpreted.

Results

XGBoost reported the best performance with ACC of 0.922, 0.859 and 0.850, AUC of 0.974, 0.924 and 0.913 for the selected level A to C models in the test set, respectively. Tree-based feature importance and SHAP method successfully identified the commonly recognized risk factors, while indicated new inconsistent impact trends for GDM in different stages of pregnancy.

Conclusion

A stepwise prediction system was successfully established. A practical tool that enables a quick prediction of GDM was released at https://github.com/ifyoungnet/MedGDM.This study is expected to provide a more detailed profiling of GDM risk and lay the foundation for the application of the model in practice.

First-trimester fasting plasma glucose as a predictor of subsequent gestational diabetes mellitus and adverse fetomaternal outcomes: A systematic review and meta-analysis

BACKGROUND

The implication of intermediately elevated fasting plasma glucose (FPG) in the first trimester of pregnancy is uncertain.

PURPOSE

The primary outcome of the meta-analysis was to analyze if intermediately elevated first-trimester FPG could predict development of GDM at 24-28 weeks. The secondary outcomes were to determine if the commonly used FPG cut-offs 5.1 mmol/L (92 mg/dL), 5.6 mmol/L (100 mg/dL), and 6.1 mmol/L (110 mg/dL) correlated with adverse pregnancy events.

DATA SOURCES

Databases were searched for articles published from 2010 onwards for studies examining the relationship between first-trimester FPG and adverse fetomaternal outcomes.

STUDY SELECTION

A total of sixteen studies involving 115,899 pregnancies satisfied the inclusion criteria.

DATA EXTRACTION AND DATA SYNTHESIS

Women who developed GDM had a significantly higher first-trimester FPG than those who did not [MD 0.29 mmoL/l (5 mg/dL); 95 % CI: 0.21-0.38; P < 0.00001]. First-trimester FPG ≥5.1 mmol/L (92 mg/dL) predicted the development of GDM at 24-28 weeks [RR 3.93 (95 % CI: 2.67-5.77); P < 0.0000], pre-eclampsia [RR 1.55 (95%CI:1.14-2.12); P = 0.006], gestational hypertension [RR1.47 (95%CI:1.20-1.79); P = 0.0001], large-for-gestational-age (LGA) [RR 1.32 (95%CI:1.13-1.54); P = 0.0004], and macrosomia [RR1.29 (95%CI:1.15-1.44); P < 0.001]. However, at the above threshold, the rates of preterm delivery, lower-segment cesarean section (LSCS), small-for gestational age (SGA), and neonatal hypoglycemia were not significantly higher. First-trimester FPG ≥5.6 mmol/L (100 mg/dL) correlated with occurrence of macrosomia [RR1.47 (95 % CI:1.22-1.79); P < 0.0001], LGA [RR 1.43 (95%CI:1.24-1.65); P < 0.00001], and preterm delivery [RR1.51 (95%CI:1.15-1.98); P = 0.003], but not SGA and LSCS.

LIMITATIONS

Only one study reported outcomes at first-trimester FPG of 6.1 mmol/L (110 mg/dL), and hence was not analyzed.

CONCLUSION

The risk of development of GDM at 24-28 weeks increased linearly with higher first-trimester FPG. First trimester FPG cut-offs of 5.1 mmol/L (92 mg/dL) and 5.6 mmol/L (100 mg/dL) predicted several adverse pregnancy outcomes.

A prediction model for cesarean delivery based on the glycemia in the second trimester: a nested case control study from two centers

OBJECTIVE

Maternal glycemia is associated with the risk of cesarean delivery (CD); therefore, our study aims to developed a prediction model based on glucose indicators in the second trimester to earlier identify the risk of CD.

METHODS

This was a nested case-control study, and data were collected from the 5th Central Hospital of Tianjin (training set) and Changzhou Second People's Hospital (testing set) from 2020 to 2021. Variables with significant difference in training set were incorporated to develop the random forest model. Model performance was assessed by calculating the area under the curve (AUC) and Komogorov-Smirnoff (KS), as well as accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

A total of 504 eligible women were enrolled; of these, 169 underwent CD. Pre-pregnancy body mass index (BMI), first pregnancy, history of full-term birth, history of livebirth, 1 h plasma glucose (1hPG), glycosylated hemoglobin (HbA1c), fasting plasma glucose (FPG), and 2 h plasma glucose (2hPG) were used to develop the model. The model showed a good performance, with an AUC of 0.852 [95% confidence interval (CI): 0.809-0.895]. The pre-pregnancy BMI, 1hPG, 2hPG, HbA1c, and FPG were identifies as the more significant predictors. External validation confirmed the good performance of our model, with an AUC of 0.734 (95%CI: 0.664-0.804).

CONCLUSIONS

Our model based on glucose indicators in the second trimester performed well to predict the risk of CD, which may reach the earlier identification of CD risk and may be beneficial to make interventions in time to decrease the risk of CD.

Establishment and evaluation of a risk prediction model for gestational diabetes mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) is a condition characterized by high blood sugar levels during pregnancy. The prevalence of GDM is on the rise globally, and this trend is particularly evident in China, which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses. Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses. Therefore, this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin, blood glucose, and body mass index (BMI) on the occurrence of GDM.

AIM

To develop a risk prediction model to analyze factors leading to GDM, and evaluate its efficiency for early prevention.

METHODS

The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed. According to whether GDM occurred, they were divided into two groups to analyze the related factors affecting GDM. Then, according to the weight of the relevant risk factors, the training set and the verification set were divided at a ratio of 7:3. Subsequently, a risk prediction model was established using logistic regression and random forest models, and the model was evaluated and verified.

RESULTS

Pre-pregnancy BMI, previous history of GDM or macrosomia, hypertension, hemoglobin (Hb) level, triglyceride level, family history of diabetes, serum ferritin, and fasting blood glucose levels during early pregnancy were de-termined. These factors were found to have a significant impact on the development of GDM (P < 0.05). According to the nomogram model's prediction of GDM in pregnancy, the area under the curve (AUC) was determined to be 0.883 [95% confidence interval (CI): 0.846-0.921], and the sensitivity and specificity were 74.1% and 87.6%, respectively. The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin, fasting blood glucose in early pregnancy, pre-pregnancy BMI, Hb level and triglyceride level. The random forest model achieved an AUC of 0.950 (95%CI: 0.927-0.973), the sensitivity was 84.8%, and the specificity was 91.4%. The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model (P < 0.05).

CONCLUSION

The random forest model is superior to the nomogram model in predicting the risk of GDM. This method is helpful for early diagnosis and appropriate intervention of GDM.

Development and validation of a nomogram to predict spontaneous preterm birth in singleton gestation with short cervix and no history of spontaneous preterm birth

Background

Spontaneous preterm birth (sPTB) stands as a leading cause of neonatal mortality. Consequently, preventing sPTB has emerged as a paramount concern in healthcare. Therefore, our study aimed to develop a nomogram, encompassing patient characteristics and cervical elastography, to predict sPTB in singleton pregnancies. Specifically, we targeted those with a short cervix length (CL), no history of sPTB, and who were receiving vaginal progesterone therapy.

Methods

A total of 568 patients were included in this study. Data from 392 patients, collected between January 2016 and October 2019, constituted the training cohort. Meanwhile, records from 176 patients, spanning November 2019 to January 2022, formed the validation cohort. Following the univariate logistic regression analysis, variables exhibiting a P-value less than 0.05 were integrated into a multivariable logistic regression analysis. The primary objective of this subsequent analysis was to identify the independent predictors linked to sPTB in the training cohort. Next, we formulated a nomogram utilizing the identified independent predictors. This tool was designed to estimate the likelihood of sPTB in singleton pregnancies, particularly those with a short CL, devoid of any sPTB history, and undergoing vaginal progesterone therapy. The C-index, Hosmer-Lemeshow (HL) test, calibration curves, decision curve analysis (DCA), and receiver operating characteristic (ROC) were used to validate the performance of the nomogram.

Results

Upon finalizing the univariate analysis, we progressed to a multivariable analysis, integrating 8 variables with P < 0.05 from the univariate analysis. The multivariable analysis identified 7 independent risk factors: maternal age (OR = 1.072; P < 0.001), cervical length (OR = 0.854; P < 0.001), uterine curettage (OR = 7.208; P < 0.001), GDM (OR = 3.570; P = 0.006), HDP (OR = 4.661; P = 0.003), C-reactive protein (OR = 1.138; P < 0.001), and strain of AI (OR = 7.985; P < 0.001). The nomogram, tailored for sPTB prediction, was grounded on these 7 independent predictors. In predicting sPTB, the C-indices manifested as 0.873 (95% CI, 0.827-0.918) for the training cohort and 0.916 (95%CI, 0.870-0.962) for the validation cohorts, underscoring a good discrimination of the model. Additionally, the ROC curves served to evaluate the discrimination of nomogram model across both cohorts. Calibration curves were delineated, revealing no statistically significant differences in both the training (χ2 = 5.355; P = 0.719) and validation (χ2 = 2.708; P = 0.951) cohorts as evidenced by the HL tests. Furthermore, the DCA underscored the model's excellence as a predictive tool for sPTB.

Conclusions

By amalgamating patient characteristics and cervical elastography data from the second trimester, the nomogram emerged as a visually intuitive and dependable tool for predicting sPTB. Its relevance was particularly pronounced for singleton pregnancies characterized by a short CL, an absence of prior sPTB incidents, and those receiving vaginal progesterone therapy.

Update on gestational diabetes and adverse pregnancy outcomes

PURPOSE OF REVIEW

To explore the recent literature concerning the effect of gestational diabetes (GDM) on adverse pregnancy outcomes (APO).

RECENT FINDINGS

Literature search on PubMed, Medline and British Journal of Obstetrics and Gynaecology was conducted using keywords. Search fields were filtered down to include articles from 2019 onwards. GDM is common during pregnancy and is on the rise because of increasing in obesity rates. GDM tended to show an increased risk of APO compared with non-GDM. Treatment of these pregnancies tended to improve these outcomes, particularly for LGA and macrosomia. Additional factors such as prepregnancy BMI and gestational weight gain (GWG) were shown to influence risk. More studies are needed to determine the true effect on postpartum haemorrhage (PPH) and induction of labour (IOL).

SUMMARY

The review agrees with the findings from previous studies and adds to the current literature. Early intervention to manage glycaemic control and GWG may help improve these outcomes. Public health strategies that tackle obesity rates will help to reduce prepregnancy BMI and, therefore, rates of GDM.

Predictive value of first-trimester GPR120 levels in gestational diabetes mellitus

Background

Early diagnosis of gestational diabetes mellitus (GDM) reduces the risk of unfavorable perinatal and maternal consequences. Currently, there are no recognized biomarkers or clinical prediction models for use in clinical practice to diagnosing GDM during early pregnancy. The purpose of this research is to detect the serum G-protein coupled receptor 120 (GPR120) levels during early pregnancy and construct a model for predicting GDM.

Methods

This prospective cohort study was implemented at the Women's Hospital of Jiangnan University between November 2019 and November 2022. All clinical indicators were assessed at the Hospital Laboratory. GPR120 expression was measured in white blood cells through quantitative PCR. Thereafter, the least absolute shrinkage and selection operator (LASSO) regression analysis technique was employed for optimizing the selection of the variables, while the multivariate logistic regression technique was implemented for constructing the nomogram model to anticipate the risk of GDM. The calibration curve analysis, area under the receiver operating characteristic curve (AUC) analysis, and the decision curve analysis (DCA) were conducted for assessing the performance of the constructed nomogram.

Results

Herein, we included a total of 250 pregnant women (125 with GDM). The results showed that the GDM group showed significantly higher GPR120 expression levels in their first trimester compared to the normal pregnancy group (p < 0.05). LASSO and multivariate regression analyses were carried out to construct a GDM nomogram during the first trimester. The indicators used in the nomogram included fasting plasma glucose, total cholesterol, lipoproteins, and GPR120 levels. The nomogram exhibited good performance in the training (AUC 0.996, 95% confidence interval [CI] = 0.989-0.999) and validation sets (AUC=0.992) for predicting GDM. The Akaike Information Criterion of the nomogram was 37.961. The nomogram showed a cutoff value of 0.714 (sensitivity = 0.989; specificity = 0.977). The nomogram displayed good calibration and discrimination, while the DCA was conducted for validating the clinical applicability of the nomogram.

Conclusions

The patients in the GDM group showed a high GPR120 expression level during the first trimester. Therefore, GPR120 expression could be used as an effective biomarker for predicting the onset of GDM. The nomogram incorporating GPR120 levels in early pregnancy showed good predictive ability for the onset of GDM.

Gut Microbiota-Derived Short Chain Fatty Acids Are Associated with Clinical Pregnancy Outcome in Women Undergoing IVF/ICSI-ET: A Retrospective Study

Gut microbiota and its metabolites are related to the female reproductive system. Animal experiments have demonstrated the relationship between gut microbiota-derived short chain fatty acids (SCFAs) and embryo quality. However, few studies have linked SCFAs to clinical pregnancy outcomes in humans. This retrospective cross-sectional study recruited 147 patients undergoing in vitro fertilization or intracytoplasmic sperm injection and embryo transfer (IVF/ICSI-ET) (70 with no pregnancies and 77 with clinical pregnancies). The association between SCFAs levels and clinical pregnancy outcomes was evaluated using univariate and multivariate logistic regression analyses. The association between SCFAs and metabolic parameters was analyzed using a linear regression model. Receiver operating characteristic (ROC) curve analysis was used for assessing the efficiency of SCFAs to evaluate the clinical pregnancy outcomes. Fecal propionate levels were significantly higher in the no pregnancy group than in the clinical pregnancy group (p < 0.01). Fecal acetate and butyrate levels were not significantly different between females with and without clinical pregnancies (p > 0.05). There were positive relationships between fecal propionate levels and fasting serum insulin (FSI) (r = 0.245, p = 0.003), Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) (r = 0.276, p = 0.001), and triglycerides (TG) (r = 0.254, p = 0.002). Multivariate analyses determined that fecal propionate (OR, 1.103; 95% CI, 1.045-1.164; p < 0.001) was an independent risk factor for no pregnancies. The area under the ROC curve (AUC) of fecal propionate was 0.702 (p < 0.001), with a sensitivity of 57.1% and a specificity of 79.2%. High fecal propionate concentration has a negative association on clinical pregnancy outcomes and is positively correlated with FSI, TG, and HOMA-IR.

Prediction of adverse pregnancy outcomes by first-trimester components of metabolic syndrome: a prospective longitudinal study

PURPOSE

This study aimed to identify the optimal cutoff values of each component of metabolic syndrome (MetS) in the first trimester of pregnancy for predicting adverse pregnancy outcomes.

METHODS

A total of 1076 pregnant women in the first trimester of gestation were recruited in this prospective longitudinal cohort study. Specifically, 993 pregnant women at 11-13 weeks of gestation who were followed up until the end of pregnancy were included in the final analysis. The cutoff values of each component of MetS in the occurrence of adverse pregnancy outcomes including gestational diabetes (GDM), gestational hypertensive disorders, and preterm birth were obtained via receiver operating characteristic (ROC) curve analysis using the Youden's index.

RESULTS

Among the 993 pregnant women studied, the significant associations between the first trimester MetS components and adverse pregnancy outcomes were as follows: triglyceride (TG) and body mass index (BMI) with preterm birth; mean arterial pressure (MAP), TG, and high-density lipoprotein cholesterol (HDL-C) with gestational hypertensive disorders; BMI, fasting plasma glucose (FPG), and TG with GDM (all p values < 0.05). The cutoff point values for the above-mentioned MetS components were: TG > 138 mg/dl and BMI < 21 kg/m² for the occurrence of preterm birth; TG > 148 mg/dL, MAP > 84, and HDL-C < 84 mg/dl for gestational hypertensive disorders; BMI > 25 kg/m², FPG > 84 mg/dl, and TG > 161 mg/dl for GDM.

CONCLUSION

The study findings imply the importance of early management of metabolic syndrome in pregnancy to improve maternal-fetal outcomes.

Continuous Glucose Monitoring System Profile of Women with Gestational Diabetes Mellitus Missed Using Isolated Fasting Plasma Glucose-Based Strategies Alternative to WHO 2013 Criteria: A Cross-Sectional Study

INTRODUCTION

The aim of the study was to evaluate the differences in the continuous glucose monitoring system (CGMS)-based glycemic parameters between women with normoglycemia and early gestational diabetes mellitus (GDM) identified on the basis of mild fasting plasma glucose elevation (FPG, 5.1-5.5 mmol/L) and/or post-load plasma glucose elevation (PLG, 1-h ≥ 10.0 mmol/L or 2-h ≥ 8.5 mmol/L).

METHODS

This cross-sectional study included women with singleton pregnancy (8⁺⁰ to 19⁺⁶ weeks of gestation) and normoglycemia or GDM per World Health Organization (WHO) 2013 criteria. We evaluated the glycemic parameters of clinical interest using blinded CGMS evaluation and reported them per standard methodology proposed by Hernandez et al. RESULTS: A total of 87 women (GDM, n = 38) were enrolled at 28.6 ± 4.5 years. Among women with GDM, 10 (26.3%) had isolated mild FPG elevation (5.1-5.5 mmol/L), 10 (26.3%) had isolated PLG elevation (1-h ≥ 10.0 mmol/L or 2-h ≥ 8.5 mmol/L), and 7 (18.4%) had a combination of both. The remaining 11 (28.9%) had elevated FPG (≥ 5.6 mmol/L) with or without PLG elevation. Thus, when an isolated FPG cutoff ≥ 5.6 mmol/L is used to diagnose GDM, 27 (71.0%) women would be perceived as normoglycemic. Such women had significantly higher CGMS parameters of clinical interest, such as 24-h mean glucose, fasting glucose, 1-h and 2-h postprandial glucose (PPG), 1-h PPG excursion, and peak PPG.

CONCLUSIONS

An isolated FPG threshold, especially the higher cutoff ≥ 5.6 mmol/L, can potentially miss a large proportion of women (nearly three-fourths) diagnosed with GDM per WHO 2013 criteria. Eventually, such women fare significantly differently from normoglycemic women in various CGMS parameters of clinical interest.

Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis

OBJECTIVE

To investigate the association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjustment for at least minimal confounding factors.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Web of Science, PubMed, Medline, and Cochrane Database of Systematic Reviews, from 1 January 1990 to 1 November 2021.

REVIEW METHODS

Cohort studies and control arms of trials reporting complications of pregnancy in women with gestational diabetes mellitus were eligible for inclusion. Based on the use of insulin, studies were divided into three subgroups: no insulin use (patients never used insulin during the course of the disease), insulin use (different proportions of patients were treated with insulin), and insulin use not reported. Subgroup analyses were performed based on the status of the country (developed or developing), quality of the study, diagnostic criteria, and screening method. Meta-regression models were applied based on the proportion of patients who had received insulin.

RESULTS

156 studies with 7 506 061 pregnancies were included, and 50 (32.1%) showed a low or medium risk of bias. In studies with no insulin use, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of caesarean section (odds ratio 1.16, 95% confidence interval 1.03 to 1.32), preterm delivery (1.51, 1.26 to 1.80), low one minute Apgar score (1.43, 1.01 to 2.03), macrosomia (1.70, 1.23 to 2.36), and infant born large for gestational age (1.57, 1.25 to 1.97). In studies with insulin use, when adjusted for confounders, the odds of having an infant large for gestational age (odds ratio 1.61, 1.09 to 2.37), or with respiratory distress syndrome (1.57, 1.19 to 2.08) or neonatal jaundice (1.28, 1.02 to 1.62), or requiring admission to the neonatal intensive care unit (2.29, 1.59 to 3.31), were higher in women with gestational diabetes mellitus than in those without diabetes. No clear evidence was found for differences in the odds of instrumental delivery, shoulder dystocia, postpartum haemorrhage, stillbirth, neonatal death, low five minute Apgar score, low birth weight, and small for gestational age between women with and without gestational diabetes mellitus after adjusting for confounders. Country status, adjustment for body mass index, and screening methods significantly contributed to heterogeneity between studies for several adverse outcomes of pregnancy.

CONCLUSIONS

When adjusted for confounders, gestational diabetes mellitus was significantly associated with pregnancy complications. The findings contribute to a more comprehensive understanding of the adverse outcomes of pregnancy related to gestational diabetes mellitus. Future primary studies should routinely consider adjusting for a more complete set of prognostic factors.

REVIEW REGISTRATION

PROSPERO CRD42021265837.

Early pregnancy hyperglycaemia as a significant predictor of large for gestational age neonates

AIMS

We aimed to determine the effect of early pregnancy hyperglycaemia on having a large for gestational age (LGA) neonate.

METHODS

A prospective cohort study was conducted among pregnant women in their first trimester. One-step plasma glucose (PG) evaluation procedure was performed to assess gestational diabetes mellitus (GDM) and diabetes mellitus (DM) in pregnancy as defined by the World Health Organization (WHO) criteria with International Association of Diabetes in Pregnancy Study Group (IADPSG) thresholds. The main outcome studied was large for gestational age neonates (LGA).

RESULTS

A total of 2,709 participants were recruited with a mean age of 28 years (SD = 5.4) and a median gestational age (GA) of eight weeks (interquartile range [IQR] = 2). The prevalence of GDM in first trimester (T1) was 15.0% (95% confidence interval [CI] = 13.7-16.4). Previously undiagnosed DM was detected among 2.5% of the participants. Out of 2,285 live births with a median delivery GA of 38 weeks (IQR = 3), 7.0% were LGA neonates. The cumulative incidence of LGA neonates in women with GDM and DM was 11.1 and 15.5 per 100 women, respectively. The relative risk of having an LGA neonate among women with DM and GDM was 2.30 (95% CI = 1.23-4.28) and 1.80 (95% CI = 1.27-2.53), respectively. The attributable risk percentage of a LGA neonate for hyperglycaemia was 15.01%. T1 fasting PG was significantly correlated with both neonatal birth weight and birth weight centile.

CONCLUSIONS

The proposed WHO criteria for hyperglycaemia in pregnancy are valid, even in T1, for predicting LGA neonates. The use of IADPSG threshold for Fasting PG, for risk assessment in early pregnancy in high-risk populations is recommended.