Diagnostic Strategies for Gestational Diabetes Mellitus: Review of Current Evidence

Does the gestational age at which the glucose challenge test (GCT) is conducted influence the diagnosis of gestational diabetes mellitus (GDM)?

PURPOSE

This study's objective is to investigate disparities in the rates of gestational diabetes mellitus (GDM) diagnosis, influenced by the timing of the glucose challenge test GCT.

METHODS

This retrospective cohort study included women with singleton or twin pregnancies exhibiting abnormal GCT result between 24 and 28 weeks of gestation, followed by an oral glucose tolerance test OGTT during the same period. Data regarding pregnancy follow-up from patients' deliveries at a singular tertiary medical from 2014 to 2022 were retrieved. The probability of GDM diagnosis was stratified based on the gestational week of the GCT and the definition of a positive OGTT, delineated by one or two abnormal values.

RESULTS

The study included 636 women with abnormal GCT between 24 and 28 weeks of gestation. Of them, 157 unerwent the GCT between 24.0 and 24.6 weeks, 204 between 25.0 and 25.6 weeks, 147 between 26.0 and 26.6 weeks, and 128 between 27.0 and 28.6 weeks. We found that the highest incidence of GDM, defined by one or two pathological values of the OGTT, following the initial screening with a GCT, where abnormal results were defined as values exceeding 140 mg/dL, was diagnosed in patients who underwent GCT between 26.0 and 26.6 weeks of gestation. Conversely, the lowest rates were observed in patients screened between 24.0 and 24.6 weeks of gestation.

CONCLUSION

The timing of screening for GDM using the GCT significantly affects the rate of diagnosis. Clinicians managing pregnancies should consider this data when formulating treatment plans.

Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms

This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks' gestation), E1 (11-13 weeks' gestation) and M1 (14-24 weeks' gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model.

Risks after Gestational Diabetes Mellitus in Taiwanese Women: A Nationwide Retrospective Cohort Study

OBJECTIVE

An increasing trend in the prevalence of gestational diabetes mellitus (GDM) has been reported in Taiwan. GDM has been linked to various adverse maternal outcomes over a long period, including cardiovascular disease (CVD) and chronic kidney disease (CKD). However, evidence implies that the effects of GDM on the mid-term surrogate risk factors for these diseases are limited. Furthermore, data from nationwide cohort studies are limited. The primary aim of this study was to investigate the risk of developing type 2 diabetes mellitus (T2DM), arterial hypertension (aHTN), and hyperlipidemia (HL) through a 5-year follow-up post-delivery of women with GDM in a nationwide cohort study in Taiwan. The second objective was to investigate the risk of developing insulin resistance syndrome (IRS)-related diseases, including CVD, acute myocardial infarction (AMI), peripheral artery occlusive disease (PAOD), non-alcoholic fatty liver diseases (NAFLD), and CKD.

METHODS

This was a retrospective, population-based nationwide cohort study. The data source comprises a merge of the Birth Certificate Application Database (BCA) and the National Health Insurance Research Database in Taiwan. Women aged between 15 and 45 years who gave birth in Taiwan between 2004 and 2011 were included. Women who were enrolled and had a GDM diagnosis were assigned to the exposure group. Women who were enrolled without a GDM diagnosis were assigned to the comparison group. The relative risk of developing T2DM, aHTN, HL, and IRS-related diseases, including CVD, AMI, PAOD, NAFLD, and CKD, were analyzed and presented as hazard ratio (HR) through Cox regression and log-rank regression analyses.

RESULTS

A total of 1,180,477 women were identified through the BCA database between 2004 and 2011. Of those, 71,611 GDM-diagnosed women and 286,444 women without GDM were included in the final analysis. After adjusting for age, pre-existing cancer, and parity, developing T2DM, aHTN, and HL were still significantly increased in the GDM group (HR and interquartile range (IQR): 2.83 (2.59, 3.08), 1.09 (1.01, 1.06), and 1.29 (1.20, 1.38), accordingly). CVD, NAFLD, and CKD had a very low incidence and showed insignificant results.

CONCLUSION

Our findings provide nationwide cohort data showing that GDM increased the risk of developing T2DM, aHTN, and HL 5 years after delivery within the same group. The GDM complications and risk of CVD, AMI, PAOD, NAFLD, and CKD need further investigation.

Metabolic alteration of circulating steroid hormones in women with gestational diabetes mellitus and the related risk factors

Background

Abnormally changed steroid hormones during pregnancy are closely related to the pathological process of gestational diabetes mellitus (GDM). Our aim was to systematically profile the metabolic alteration of circulating steroid hormones in GDM women and screen for risk factors.

Methods

This study was a case-control study with data measured from 40 GDM women and 70 healthy pregnant women during their 24-28 gestational weeks. 36 kinds of steroid hormones, including 3 kinds of corticosteroids, 2 kinds of progestins, 5 kinds of androgens and 26 kinds of downstream estrogens in serum were systematically measured using a combined sensitive UPLC-MS/MS method. The flux of different metabolic pathways of steroid hormones was analyzed. Logistic regression and ROC curve model analyses were performed to identify potential steroid markers closely associated with GDM development.

Results

Serum corticosteroids, progestins and almost all the estrogen metabolites via 16-pathway from parent estrogens were higher in GDM women compared with healthy controls. Most of the estrogen metabolites via 4-pathway and more than half of the metabolites via 2-pathway were not significantly different. 16α-hydroxyestrone (16OHE1), estrone-glucuronide/sulfate (E1-G/S) and the ratio of total 2-pathway estrogens to total estrogens were screened as three indicators closely related to the risk of GDM development. The adjusted odds ratios of GDM for the highest quartile compared with the lowest were 72.22 (95% CI 11.27-462.71, P _trend <0.001) for 16OHE1 and 6.28 (95% CI 1.74-22.71, P _trend <0.05) for E1-G/S. The ratio of 2-pathway estrogens to total estrogens was negatively associated with the risk of GDM.

Conclusion

The whole metabolic flux from cholesterol to downstream steroid hormones increased in GDM condition. The most significant changes were observed in the 16-pathway metabolism of estrogens, rather than the 2- or 4-pathway or other types of steroid hormones. 16OHE1 may be a strong marker associated with the risk for GDM.

Trends in epidemiology of hyperglycemia in pregnancy in Taiwan, 2008-2017

Background

Hyperglycemia in pregnancy (HIP) increases the risk of adverse pregnancy outcomes. The increasing prevalence of overweight or obesity and the increasing proportion of pregnant women with advanced maternal age (AMA) in the recent decade may affect its prevalence. We analyzed the secular trend of HIP prevalence in 2008-2017 in Taiwan and investigated the impact of AMA in this study.

Methods

This cross-sectional study used data from Health and Welfare Data Science Center. Pregnant women who registered their data in the Birth Certificate Application in 2008-2017 were recruited. Diagnosis of HIP was defined by ICD-9-CM and ICD-10-CM codes.

Results

In 2008-2017, 151,306-211,768 pregnant women were recruited in different years. The proportion of women with AMA increased from 15.8% to 32.1%. Meanwhile, the prevalence increased from 0.5% to 0.9% for preexisting diabetes, 0.2% to 0.4% for undiagnosed diabetes, and 11.4% to 14.5% for GDM. Maternal age was significantly associated with the prevalence of HIP. For women aged <30 years, 30-34 years and ≥35 years, the prevalence of preexisting diabetes were 0.51%, 0.75% and 1.24%, respectively (p<0.05); the prevalence of undiagnosed diabetes were 0.18%, 0.24% and 0.37%, respectively (p<0.05); and the prevalence of GDM were 10.57%, 14.77% and 18.13%, respectively (p<0.05). In all age groups, the prevalence of HIP increased over time in 2008-2017.

Conclusion

The prevalence of HIP increased in Taiwan in 2008-2017, which may result from the increasing proportion of pregnant women with AMA and the change in the diagnostic criteria for GDM.

Diagnosing Gestational Diabetes with a Probably Too Simplified Diagnostic Procedure Compared to International Criteria: The Indian Case Study

Diagnostic procedures for the diagnosis of gestational diabetes mellitus (GDM) are not uniformly defined worldwide. We retrospectively applied two diagnostic procedures (i.e., the IADPSG and the Indian) to the same pregnant women in order to compare the clinical characteristics and the prevalence of risk factors for GDM. Overall, 1015 pregnant women were evaluated. GDM was diagnosed in 113 cases (11.1%) by the IADPSG criteria and in 105 cases (10.3%) by the Indian criteria. The women diagnosed with GDM according to the IADPSG criteria had higher pre-gestational BMIs, higher previous macrosomia rates, higher first trimester fasting blood glucose levels, higher fasting and 1 h glucose levels after glucose load at OGTT, and lower 2 h glucose levels at OGTT compared with the women with GDM diagnosed according to the Indian criteria. Only 49.6% of the women who were diagnosed by the IADPSG criteria were also diagnosed with GDM by the Indian diagnostic criteria. For 47.8% of the women who were diagnosed by the IADPSG criteria, a diagnosis of GDM was missed by applying the Indian diagnostic criteria. Interestingly, 49 women were diagnosed with GDM by the Indian criteria but were normal according to the IADPSG criteria. Different diagnostic criteria could lead to different GDM detection rates with different practical approaches.

The Analytical Reliability of the Oral Glucose Tolerance Test for the Diagnosis of Gestational Diabetes: An Observational, Retrospective Study in a Caucasian Population

The Oral Glucose Tolerance Test (OGTT) is currently the gold standard reference test for the diagnosis of gestational diabetes mellitus (GDM). Several critical issues related to analytical variables have challenged its reproducibility and accuracy. This study aimed to assess the analytical reliability of the OGTT for the diagnosis of GDM. A total of 1015 pregnant women underwent a 2 h 75 g OGTT between 24 and 28 weeks of gestation. As recommended by National Academy of Clinical Biochemistry, we considered the total maximum allowable error for glucose plasma measurement as <6.9%. Assuming the possibility of analytical errors within this range for each OGTT glucose plasma value, different scenarios of GDM occurrence were estimated. GDM prevalence with standard criteria was 12.2%, and no hypothetical scenarios have shown a comparable GDM prevalence. Considering all the three OGTT values estimated at the lowest or the highest allowed value according to total maximum allowable error, GDM prevalence significantly varied (4.5% and 25.3%, respectively). Our results indicate that the OGTT is not completely accurate for GDM diagnosis.

Impact of Risk Factors on Short and Long-Term Maternal and Neonatal Outcomes in Women With Gestational Diabetes Mellitus: A Prospective Longitudinal Cohort Study

AIMS

Universal screening of gestational diabetes mellitus (GDM) in women with no risk factors (RF) for GDM remains controversial. This study identified the impact of the presence of RF on perinatal and postpartum outcomes.

METHODS

This prospective cohort study included 780 women with GDM. GDM RF included previous GDM, first grade family history of type 2 diabetes, high-risk ethnicity and pre-pregnancy overweight/obesity (OW/OB). Outcomes included obstetrical, neonatal and maternal metabolic parameters during pregnancy and up to 1 year postpartum.

RESULTS

Out of 780 patients, 24% had no RF for GDM. Despite this, 40% of them needed medical treatment and they had a high prevalence of glucose intolerance of 21 and 27% at 6-8 weeks and 1-year postpartum, respectively. Despite similar treatment, women with RF had more neonatal and obstetrical complications, but they had especially more frequent adverse metabolic outcomes in the short- and long-term. The most important RF for poor perinatal outcome were previous GDM and pre-pregnancy OW/OB, whereas high-risk ethnicity and pre-pregnancy OW/OB were RF for adverse postpartum metabolic outcomes. Increasing number of RF were associated with worsened perinatal and long-term postpartum outcomes except for pregnancy-induced hypertension, C-section delivery and neonatal hypoglycaemia.

CONCLUSION

Women with no RF had a high prevalence of adverse perinatal and postpartum outcomes, while the presence of RF particularly increased the risk for postpartum adverse metabolic outcomes. This calls for a RF-based long-term follow-up of women with GDM.

Impact of the Gestational Diabetes Diagnostic Criteria during the Pandemic: An Observational Study

OBJECTIVE

To analyze the effect of applying alternative diagnostic criteria for gestational diabetes mellitus (GDM) during the COVID-19 pandemic on GDM prevalence and obstetrical and perinatal outcomes, in comparison to usual diagnostic approaches.

METHODS

Data from women referred to GDM diagnosis from 1 September to 30 November 2019 were retrospectively collected (2019-group). The same data from the same period in 2020 were prospectively collected (2020-group). In both cases, a two-step diagnostic approach was used, the first step being a screening test (1 h 50 goral glucose tolerance test, OGTT). In 2019 it was followed by a 100 gr OGTT for diagnosis. In 2020, this was replaced by a blood test for the measurement of plasma glucose and HbA1c, according to alternative GDM diagnostic criteria during the COVID-19 pandemic.

RESULTS

From 237 women in the 2019 group, 40 (16.9%) were diagnosed with GDM, while from 255 women in the 2020 group, 37 (14.5%) had GDM (p = 0.470). More women in the 2020 group, in comparison to the 2019 group, were nulligravid (41.9% vs. 47.2%, p = 0.013), had a personal history of GDM (11.4% vs. 4.6%, p = 0.013) and had macrosomia in previous pregnancies (10.2% vs. 2.1%, p = 0.001). Obstetrical and perinatal outcomes were similar when comparing women with GDM to non-GDM women in the 2019 and 2020 groups and between GDM women and non-GDM women.

CONCLUSION

In a Spanish population, GDM prevalence during the COVID-19 pandemic using the alternative diagnostic criteria was similar to that found in 2019 using the usual diagnostic criteria. Despite women referred for GDM diagnosis during the pandemic having more GDM risk factors, obstetrical and perinatal outcomes were comparable to those observed before the pandemic.

Validation of ICD-10 Codes for Gestational and Pregestational Diabetes During Pregnancy in a Large, Public Hospital

BACKGROUND

The use of billing codes (ICD-10) to identify and track cases of gestational and pregestational diabetes during pregnancy is common in clinical quality improvement, research, and surveillance. However, specific diagnoses may be misclassified using ICD-10 codes, potentially biasing estimates. The goal of this study is to provide estimates of validation parameters (sensitivity, specificity, positive predictive value, and negative predictive value) for pregestational and gestational diabetes diagnosis using ICD-10 diagnosis codes compared with medical record abstraction at a large public hospital in Atlanta, Georgia.

METHODS

This study includes 3,654 deliveries to Emory physicians at Grady Memorial Hospital in Atlanta, Georgia, between 2016 and 2018. We linked information abstracted from the medical record to ICD-10 diagnosis codes for gestational and pregestational diabetes during the delivery hospitalization. Using the medical record as the gold standard, we calculated sensitivity, specificity, positive predictive value, and negative predictive value for each.

RESULTS

For both pregestational and gestational diabetes, ICD-10 codes had a high-negative predictive value (>99%, Table 3) and specificity (>99%). For pregestational diabetes, the sensitivity was 85.9% (95% CI = 78.8, 93.0) and positive predictive value 90.8% (95% CI = 85, 97). For gestational diabetes, the sensitivity was 95% (95% CI = 92, 98) and positive predictive value 86% (95% CI = 81, 90).

CONCLUSIONS

In a large public hospital, ICD-10 codes accurately identified cases of pregestational and gestational diabetes with low numbers of false positives.

Comparison of Pregnancy Outcomes Using Different Gestational Diabetes Diagnostic Criteria and Treatment Thresholds in Multiethnic Communities between Two Tertiary Centres in Australian and New Zealand: Do They Make a Difference?

Introduction: Australia, but not New Zealand (NZ), has adopted the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria to diagnose gestational diabetes (GDM). We compared pregnancy outcomes using these different diagnostic approaches. Method: Prospective data of women with GDM were collected from one NZ (NZ) and one Australian (Aus) hospital between 2007–2018. Aus screening criteria with 2-step risk-based 50 g Glucose Challenge Testing (GCT) followed by 75 g-oral glucose tolerance testing (OGTT): fasting ≥ 5.5, 2-h ≥ 8.0 mmol/L (ADIPS98) changed to a universal OGTT and fasting ≥5.1, 1-h ≥ 10, 2-h ≥ 8.5 mmol/L (IADPSG). NZ used GCT followed by OGTT with fasting ≥ 5.5, 2-h ≥ 9.0 mmol/L (NZSSD); in 2015 adopted a booking HbA1c (NZMOH). Primary outcome was a composite of macrosomia, perinatal death, preterm delivery, neonatal hypoglycaemia, and phototherapy. An Aus subset positive using NZSSD was also defined. RESULTS: The composite outcome odds ratio compared to IADPSG (1788 pregnancies) was higher for NZMOH (934 pregnancies) 2.227 (95%CI: 1.84–2.68), NZSSD (1344 pregnancies) 2.19 (1.83–2.61), and ADIPS98 (3452 pregnancies) 1.91 (1.66–2.20). Composite outcomes were similar between the Aus subset and NZ. Conclusions: The IADPSG diagnostic criteria were associated with the lowest rate of composite outcomes. Earlier NZ screening with HbA1c was not associated with a change in adverse pregnancy outcomes.

Screening for Gestational Diabetes during the COVID-19 Pandemic-Current Recommendations and Their Consequences

Gestational diabetes mellitus (GDM) is recognized as one of the most common medical complications of pregnancy that can lead to significant short-term and long-term risks for the mother and the fetus if not detected early and treated appropriately. Current evidence suggests that, with the use of appropriate screening programs for GDM, those women diagnosed and treated have reduced perinatal morbidity. It has been implied that, when screening for GDM, there should be uniformity in the testing used and in further management. This paper summarizes and compares current screening strategies proposed by international bodies and discusses application in the context of the COVID-19 pandemic.

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

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

Simplifying the screening of gestational diabetes by maternal age plus fasting plasma glucose at first prenatal visit: A prospective cohort study

AIM

The addition of maternal age to fasting plasma glucose (FPG) at 24-28 gestational weeks improves the performance of GDM screening as maternal age increases. However, this method delays the diagnosis of GDM. Since FPG at the first prenatal visit (FPV) is a screening option for pre-existing diabetes, we evaluated the performance of age plus FPG at the FPV to reduce the need for the OGTT.

METHODS

Pregnant women were recruited consecutively in 2013-2018 (the training cohort) and 2019 (the validation cohort). We excluded women with twin pregnancies, unavailable FPG at the FPV or OGTT data, pre-pregnancy diabetes, or a history of GDM. All participants underwent FPG and haemoglobin A1c (HbA1c) at the FPV and received 75-g OGTT at 24-28 gestational weeks if FPG at the FPV was <92 mg/dL. GDM was diagnosed by the IADPSG criteria. Two algorithms were developed with the cutoffs determined when the percentage requiring OGTT (OGTT%) was the lowest and the sensitivity was ≥90%.

RESULTS

The incidence of GDM increased with age. The "FPG at the FPV" algorithm reduced OGTT% to 68.8% with the FPG cutoff at 79 mg/dl. The "age plus FPG at the FPV" algorithm, with the cutoff of 114, further reduced OGTT% to 58.3%, with the sensitivity of 90.7% (9.3% GDM missed) and the specificity of 100%. These findings were replicated in the validation cohort.

CONCLUSIONS

Screening GDM by maternal age plus FPG at the FPV can reduce OGTT%, especially in populations with a significant proportion of pregnant women with advanced ages.

Gestational Diabetes Mellitus Screening and Diagnosis

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

Appropriate Timing of Gestational Diabetes Mellitus Diagnosis in Medium- and Low-Risk Women: Effectiveness of the Italian NHS Recommendations in Preventing Fetal Macrosomia

BACKGROUND

Screening strategies for gestational diabetes mellitus (GDM) earlier than 24-28 weeks of gestation should be considered to prevent adverse pregnancy outcomes. Nonetheless, there is uncertainty about which women would benefit most from early screening and which screening strategies should be offered to women with GDM. The Italian National Healthcare Service (NHS) recommendations on selective screening for GDM at 16-18 weeks of gestation are effective in preventing fetal macrosomia in high-risk (HR) women, but the appropriateness of timing and effectiveness of these recommendations in medium- (MR) and low-risk (LR) women are still controversial. Patients and Methods. We retrospectively enrolled 769 consecutive singleton pregnant women who underwent both anomaly scan at 19-21 weeks of gestation and screening for GDM at 16-18 and/or 24-28 weeks of gestation, in agreement with the NHS recommendations and risk stratification criteria. Comparison of maternal characteristics, fetal biometric parameters at anomaly scan (head circumference (HC), biparietal diameter (BPD), abdominal circumference (AC), femur length (FL), estimated fetal weight (EFW)), and neonatal birth weight (BW) percentile among risk groups was examined.

RESULTS

219 (28.5%) women were diagnosed with GDM, while 550 (71.5%) were normal glucose-tolerant women. Out of 164 HR women, only 62 (37.8%) underwent the recommended early screening for GDM at 16-18 weeks of gestation. AC and EFW percentiles, as well as neonates' BW percentiles, were significantly higher in HR women diagnosed with GDM at 24-28 weeks of gestation with respect to normal glucose-tolerant women, as well as MR and LR women who tested positive for GDM. Comparative analysis between MR and LR women with GDM and women with normal glucose tolerance revealed significant differences in both AC and EFW percentiles (P < 0.05), while there was no significant difference in neonatal BW percentiles.

CONCLUSION

In MR and LR women with GDM, a mild acceleration of fetal growth can be detected at the time of anomaly scan. However, in these at-risk categories, the NHS recommendations for screening and treatment of GDM at 24-28 weeks of gestation are still effective in normalizing BW and preventing fetal macrosomia, thus supporting a risk factor-based selective screening program for GDM.