Detection Of Gestational Diabetes Mellitus Research Articles

FASTING BLOOD SUGAR (FBS) IN THE FIRST TRIMESTER: PREDICTIVE VALUE FOR GESTATIONAL DIABETES MELLITUS AND IMPACT ON PREGNANCY-RELATED OUTCOMES

Gestational diabetes mellitus (GDM) is a significant health concern in pregnancy, affecting maternal and neonatal outcomes. Early detection of GDM through fasting blood glucose (FBS) screening may help in timely management to prevent complications. Objective: To determine the fasting blood glucose threshold that accurately predicts the onset of gestational diabetes mellitus (GDM) and its impact on pregnancy outcomes. Methods: A prospective cohort study was conducted at the Obstetrics and Gynecology Department of Jinnah Postgraduate Medical Centre (JPMC), Karachi, from August 2023 to May 2024, involving 405 pregnant women selected through systematic random sampling. Inclusion criteria included singleton pregnancies, excluding women with pregestational diabetes or baseline fasting glucose levels of 126 mg/dL or higher. Baseline data on age, BMI, gestational age, and family history of diabetes were collected, and FBS levels were measured at the initial visit. Oral Glucose Tolerance Tests (OGTT) were conducted between 24 to 28 weeks of gestation to diagnose GDM. Maternal and fetal outcomes were monitored until delivery. Statistical analyses, including logistic regression and ROC curve analysis, were performed using IBM SPSS version 20. Results: Among the 405 participants, with a mean age of 28 ±6.49 years, the mean fasting blood sugar (FBS) was 89.5 mg/dL (±12.3). Impaired FBS (≥92 mg/dL) was noted in 24.70% of participants, and 18.27% were diagnosed with GDM following OGTT. A fasting glucose level of 127.4 mg/dL (±22.1) was observed among women diagnosed with GDM. Logistic regression showed a significant association between FBS ≥92 mg/dL and GDM risk (OR 1.08, CI 1.02-1.14, p=0.011). ROC analysis revealed an FBS cutoff of 92 mg/dL with 73% sensitivity and 68% specificity for predicting GDM. Gestational hypertension was observed in 8.64% of participants, and preeclampsia in 6.67%. Vaginal deliveries occurred in 72.1% of cases, while 27.9% required cesarean sections. Neonatal outcomes showed an 11% macrosomia rate, with 12% requiring NICU admission. Conclusion: Fasting blood glucose levels ≥92 mg/dL in early pregnancy are strongly predictive of gestational diabetes mellitus (GDM). Early FBS screening allows for timely intervention, potentially improving maternal and neonatal outcomes. This study underscores the importance of incorporating FBS screening in routine antenatal care for better pregnancy management.

HYGIENIC SUBSTANTIATION OF EARLY DETECTION AND PREVENTION OF RISK FACTORS FOR DIABETES IN PREGNANT WOMEN

Gestational Diabetes Mellitus (GDM) is a condition that develops during pregnancy and poses significant health risks to both mother and baby if not detected and managed early. This article explores the importance of hygienic practices in the early detection and prevention of GDM. It highlights key factors, including regular prenatal check-ups, the role of nutritional and personal hygiene, and the need for consistent blood glucose monitoring. Additionally, it emphasizes lifestyle modifications for at-risk women, such as maintaining a balanced diet and proper sleep hygiene. The article concludes with recommendations for overcoming common challenges in managing GDM through education and community awareness programs, underlining the critical role of proactive healthcare in ensuring healthy pregnancies.

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.

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

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. 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. 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 ( -0.52, 95% CrI [-0.61, -0.46]). 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.

Public Perception of the Food Estate Program of Cassava Cultivation as a Strategic Logistic Reserve in Realizing Regional Food Security in Gunung Mas Regency, Central Kalimantan

Gestational Diabetes Mellitus (GDM) is a collection of symptoms that arise in pregnant women caused by an increase in blood glucose levels due to a progressive decrease in insulin secretion, variables that influence the incidence of gestational diabetes include age, history of diabetes mellitus, can occur at any time, but this disease usually begins to attack in the 24th week of pregnancy. The prevalence of Gestational Diabetes Mellitus in Indonesia is still relatively small, namely around 3-5%, but this figure could be higher because cases of GDM are rarely detected. Even though it can heal on its own, that doesn't mean gestational diabetes is not dangerous. If not treated properly, this disease can increase the risk of the baby being born with excess weight, being born prematurely, or being born with low blood sugar or hypoglycemia. For pregnant women, gestational diabetes has the potential to cause complications, such as preeclampsia and hypertension. In Indonesia, all pregnant women are recommended to undergo DMG screening. However, not many pregnant women do this. The aim of this research is to determine the relationship between the perceptions of pregnant women and the behavior of early detection of Gestational Diabetes Mellitus at the Plumbon health center, Indramayu Regency in 2024. This research is quantitative research with a cross-sectional study design. The respondents in this study were 107 pregnant women who lived in the working area of the Plumbon Health Center, Indramayu Regency and used health services at the health center in 2024. Samples were taken using the Accidental Sampling technique, a technique for determining samples based on chance, namely any patient who happened to meet the researcher. and data were analyzed univariate and bivariate. The results of the research show that the characteristics of respondents, namely age, education, employment and income, do not have a significant relationship with early detection behavior of GDM, however, the perception of pregnant women with behavior of early detection of Gestational Diabetes Mellitus has a significant relationship as does mother's knowledge of early detection of Gestational Diabetes Mellitus. have a significant relationship.

Utilizing Continuous Glucose Monitoring for Early Detection of Gestational Diabetes Mellitus and Pregnancy Outcomes in an Asian Population.

We explored the potential value of continuous glucose monitoring (CGM) in early pregnancy in predicting gestational diabetes mellitus (GDM) and pregnancy outcomes. The study recruited 103 multiethnic Asian pregnant women with overweight or obesity from a hospital-based, prospective cohort. All of them had worn blinded CGM devices in early pregnancy and underwent the universal GDM screening at 24-28 gestation weeks. Models were selected based on early pregnancy risk factors and CGM-derived parameters to compare their respective predictive values for GDM and pregnancy outcomes. Eighteen GDM cases were ascertained. CGM-derived novel parameters demonstrated greater performance (e.g., area under the curve: 0.953 vs. 0.722) for predicting incident GDM compared with the model using traditional risks. Such novel CGM-derived parameters significantly differentiated primary cesarean and large-for-gestational age babies. Our data suggest CGM's potential clinical utility in the first trimester for predicting GDM and adverse pregnancy outcomes, particularly in individuals with overweight or obesity.

Unravelling the Metabolic Underpinnings of Gestational Diabetes Mellitus: A Comprehensive Mendelian Randomisation Analysis Identifying Causal Metabolites and Biological Pathways.

Gestational diabetes mellitus (GDM) has a strong genetic predisposition. Integrating metabolomics with Mendelian randomisation (MR) analysis offers a potent method to uncover the metabolic factors causally linked to GDM pathogenesis. This study aims to identify specific metabolites and metabolic pathways causally associated with GDM susceptibility through a comprehensive MR analysis. Additionally, it seeks to explore the potential of these identified metabolites as circulating biomarkers for early GDM detection and risk assessment. Furthermore, it aims to evaluate the implicated metabolic pathways as potential therapeutic targets for preventive or interventional strategies against GDM. A two-sample MR study was conducted using summary statistics from a metabolite genome-wide association study (GWAS) of 8299 individuals and a GDM GWAS comprising 13,039 cases and 197,831 controls. Rigorous criteria were applied to select robust genetic instruments for 850 metabolites. MR analysis revealed 47 metabolites exhibiting putative causal associations with GDM risk. Among these, five metabolites demonstrated statistically significant associations after multiple-testing correction: Beta-citrylglutamate, Isobutyrylcarnitine (c4), 1,2-dilinoleoyl-GPC (18:2/18:2), Alliin and Cis-3,4-methyleneheptanoylcarnitine. Importantly, all these metabolites exhibited protective effects against GDM development. Additionally, metabolic pathway enrichment analysis implicated the methionine metabolism and spermidine and spermine biosynthesis pathways in the pathogenesis of GDM. This comprehensive MR study has robustly identified specific metabolites and metabolic pathways with causal links to GDM susceptibility. These findings provide novel insights into the metabolic underpinnings of GDM aetiology and offer promising translational implications. The identified metabolites could serve as potential circulating biomarkers for early detection and risk stratification, while the implicated metabolic pathways may represent therapeutic targets for preventive or interventional strategies against GDM.

Deciphering DNA Methylation in Gestational Diabetes Mellitus: Epigenetic Regulation and Potential Clinical Applications.

Gestational diabetes mellitus (GDM) represents a prevalent complication during pregnancy, exerting both short-term and long-term impacts on maternal and offspring health. This review offers a comprehensive outline of DNA methylation modifications observed in various maternal and offspring tissues affected by GDM, emphasizing the intricate interplay between DNA methylation dynamics, gene expression, and the pathogenesis of GDM. Furthermore, it explores the influence of environmental pollutants, maternal nutritional supplementation, and prenatal gut microbiota on GDM development through alterations in DNA methylation profiles. Additionally, this review summarizes recent advancements in DNA methylation-based diagnostics and predictive models in early GDM detection and risk assessment for subsequent type 2 diabetes. These insights contribute significantly to our understanding of the epigenetic mechanisms underlying GDM development, thereby enhancing maternal and fetal health outcomes and advocating further efforts in this field.

Circulating glycated albumin levels and gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance that is first diagnosed during pregnancy, making it the most common complication associated with this period. Early detection and targeted treatment of GDM can minimize foetal exposure to maternal hyperglycaemia and subsequently reduce the associated adverse pregnancy outcomes. Previous studies have inconsistently suggested that the level of glycated albumin (GA) might predict GDM. To review and synthesize existing evidence to evaluate the relationship between GA levels and the development of GDM. We sought to compare GA levels between GDM and control groups in this meta-analysis by systematically searching the Web of Science, PubMed, Cochrane Library, and Embase databases for articles published up to June 2023. The analysis utilized the weighted mean difference (WMD) as the primary metric. The data were meticulously extracted, and the quality of the included studies was assessed. Additionally, we conducted a subgroup analysis based on study region and sample size. We assessed heterogeneity using I 2 statistics and evaluated publication bias through funnel plots. Additionally, trim-and-fill analysis was employed to detect and address any potential publication bias. The meta-analysis included a total of 11 studies involving 5477 participants, comprising 1900 patients with GDM and 3577 control individuals. The synthesized results revealed a notable correlation between elevated GA levels and increased susceptibility to GDM. The calculated WMD was 0.42, with a 95% confidence interval (95%CI) ranging from 0.11 to 0.74, yielding a P value less than 0.001. Concerning specific GA levels, the mean GA level in the GDM group was 12.6, while for the control group, it was lower, at 11.6. This discrepancy underscores the potential of GA as a biomarker for assessing GDM risk. Moreover, we explored the levels of glycated haemoglobin (HbA1c) in both cohorts. The WMD for HbA1c was 0.19, with a 95%CI ranging from 0.15 to 0.22 and a P value less than 0.001. This observation suggested that both GA and HbA1c levels were elevated in individuals in the GDM group compared to those in the control group. Our meta-analysis revealed a substantial correlation between elevated GA levels and increased GDM risk. Furthermore, our findings revealed elevated levels of HbA1c in GDM patients, emphasizing the significance of monitoring both GA and HbA1c levels for early GDM detection and effective management.

1239-P: Utilizing Continuous Glucose Monitoring for Early Detection of Gestational Diabetes Mellitus in a Multiethnic Asian Population

1239-P: Utilizing Continuous Glucose Monitoring for Early Detection of Gestational Diabetes Mellitus in a Multiethnic Asian Population

The serum leptin can be a predictable marker for gestational diabetes mellitus

This study assessed the serum leptin concentrations for development of gestational diabetes mellitusThis is a case-control study, included 120 participants (60: Gestational Diabetes Mellitus Cases and 60: Healthy Controls). We assed BMI, FBS, HbA1c, Lipid Profile and serum leptin. The serum leptin concentrations were significantly elevated in patients with gestational diabetes mellitus when compared to controls (P=0.001**). There was a significant positive correlation between the serum leptin and BMI, FBS, HbA1c, Total Cholesterol and LDL (P=0.001**). Based on study findings, significant elevated levels of serum leptin might be served as a marker for detection of gestational diabetes mellitus.

HbA1c as an early diagnostic marker in gestational diabetes mellitus.

Objective: To assess the effectiveness of HbA1c as an indicator of Gestational Diabetes mellitus(GDM). Study Design: Prospective Cases Control study. Setting: Fauji Foundation Hospital Rawalpindi. Period: March 2022 to Oct 2022. Methods: All pregnant females older than 18 years of age having pregnancy confirmed by gynecologist in 1st trimester were included. A 5ml blood sample was obtained from each case to evelaute the HbA1c level. Oral glucose tolerance test (OGTT) was performed on both groups at conception to 12 weeks (1st trimester) as well as at 12 to 24 weeks of gestation (2nd trimester) to evaluate correlation between OGTT and HbA1c test. All the females suffering from GDM were taken in cases group and comparatively in equal number non GDM pregnant women were taken in control group. All the relevant information was collected via study proforma and SPSS version 26 was used for analysis. Results: The number of females suffering from GDM was 45 in number and we took 45 pregnant females as control group. Average level of HbA1c in women with GDM (test group) was higher than in women with Non-GDM (control group). Patients having age groups 35-39 showing greater prevalence of gestational diabetes with 73.2% sensitivity and 66.7% specificity at a cut-off value of 5.6% (31 mmol/mol) for GDM diagnosis. Conclusion: The findings reveal significantly elevated HbA1c levels during both the first and second trimesters in individuals diagnosed with GDM, with a test specificity of 95%. This underscores its ability to accurately pinpoint individuals who truly have GDM. However, HbA1c is observed as a valuable diagnostic tool for the early detection of GDM.

PREVALENCE OF GESTATIONAL DIABETES AND RELATED COMPLICATIONS IN DISTRICT SIALKOT

Gestational diabetes mellitus (GDM) is a prevalent medical complication during pregnancy, associated with adverse maternal and neonatal outcomes. Understanding the impact of GDM on pregnancy complications is essential for effective management and care. Objective: This study aimed to compare the incidence of various complications between pregnant women with and without GDM. Method: A total of 200 pregnant women were recruited from Govt. Sardar Begum Teaching Hospital Sialkot and Civil Hospital Sialkot. These patients were given 75 g oral glucose irrespective of the meals, and their plasma glucose was estimated at two h. Patients with plasma glucose values of 140 mg/dl were labelled GDM; the rest were the control or non-GDM groups. All GDM patients were followed up and treated with diet and insulin therapy till delivery to know maternal and fetal outcomes. Results: Among the enrolled pregnant women, 70 (35%) were diagnosed with GDM. Significant differences were observed between the Non-GDM and GDM groups in terms of Cesarean section delivery (23.8% vs 55.7%, P=0.001), hypertension (13.8% vs 74.3%, P=0.031), and preterm birth (17.7% vs 24.3%, P=0.001). However, no significant difference was found in the incidence of fetal abnormalities between the two groups (9.2% vs. 8.6%, P=0.012). Additionally, the risk of intrauterine fetal demise was comparable between the Non-GDM and GDM groups (10.0% vs. 11.4%, P=0.031). Conclusion: Pregnant women with GDM are at increased risk of Cesarean section delivery, hypertension, and preterm birth compared to those without GDM. However, the incidence of fetal abnormalities and intrauterine fetal demise does not significantly differ between the two groups. Early detection and management of GDM are crucial to minimise adverse pregnancy outcomes.

Exploring the influence of microbiota on gestational diabetes and its potential as a biomarker

Gestational diabetes mellitus (GDM) represents a significant health concern during pregnancy, impacting both maternal and fetal well-being. While conventional diagnostic protocols typically rely on blood glucose levels in the latter stages of pregnancy, there is a pressing need for early detection methods to mitigate potential risks. A plethora of glucose-based or non-glucose-based biomarkers have been investigated for their potential to predict GDM in early pregnancy. Though specific biomarkers showed promise in predicting GDM, their clinical usage has been constrained by the lack of validation and limitation in translating them into routine clinical use. This review aims to highlight and discuss the potential and practical utility of existing biomarkers and emergent biomarkers, such as microbiomes, in diagnosing GDM. A comprehensive analysis of recent studies reveals significant alterations in the composition and diversity of microbiota among women with GDM, suggesting their potential utility as predictive markers for this condition. For instance, distinct microbial profiles characterized by an increased abundance of Eisenbergiella, Tyzzerella 4, and Lachnospiraceae NK4A136, alongside decreased levels of Parabacteroides, Parasutterella, and Ruminococcaceae UCG 002, correlated with fasting blood glucose levels, hinting at their relevance in early GDM detection. Furthermore, proposed microbiota-targeted panels demonstrated promising predictive accuracy. Beyond gut microbiota, recent investigations have also explored the potential of oral microbiota as predictive biomarkers for GDM. Studies have highlighted the discriminatory capacity of specific oral microbes, such as Streptococcus in saliva and Leptotrichia in dental plaque, in distinguishing GDM from healthy pregnancies. Moreover, the examination of gut microbiota-derived metabolites has shown promising results in serum-based GDM prediction. These findings collectively underscore the potential of microbiota and its metabolites as valuable biomarkers for the early detection of GDM. However, further research is warranted to elucidate the mechanistic links between microbial dysbiosis and GDM pathogenesis, ultimately facilitating the development of targeted therapeutic interventions and personalized management strategies.

Machine Learning for Early Diabetes Detection and Diagnosis

In this work, a machine learning (ML)-based e-diagnostic system is suggested specifically for the detection of gestational diabetes mellitus (GDM). Reviewing recent GDM data and outlining the intimate connection between GDM and prediabetic conditions, as well as the potential for future declines in insulin resistance and the emergence of overt Type 2 diabetes, were our goals. The present study explores the application of the K-nearest neighbors (KNN) algorithm to project diabetes diagnosis on the widely-used Pima Indians Diabetes database. The KNN algorithm, a non-parametric, instance-based learning method, was employed to classify individuals as either diabetic or non-diabetic, our objectives were to evaluate the algorithm’s ability to make accurate predictions and explore factors influencing its performance. The study commenced with data preprocessing, including handling missing values, feature scaling, and data splitting into training and testing sets. The KNN classifier was trained and tested using these best-fit parameters. The results of this study revealed a model with an accuracy of approximately 0.76 in predicting diabetes diagnosis. This study looked at the various machine-learning approaches for diabetes patient classification, including recall, accuracy, precision, and F1-score. The study discusses the significance of hyperparameter tuning, data preprocessing, and imbalanced data handling in achieving optimal KNN model performance. Lastly, this study shows how the KNN algorithm may be used to project diabetes using the Pima Indians Diabetes Database. The findings suggest that KNN can serve as a viable tool in the early detection of diabetes, paving the way for more extensive applications in healthcare and predictive modelling.

The Relationship between Iron Markers, Cortisol and Insulin Resistance in Gestational Diabetes Mellitus

Background: Gestational diabetes mellitus (GDM) is a significant metabolic disorder occurring during pregnancy, associated with serious maternal and fetal complications if left untreated. The global prevalence of GDM emphasizes the urgent need for research into its pathogenesis and contributing factors, including hormonal changes, insulin resistance, and the role of iron metabolism. Objective: The aim of this study was to investigate the relationship between iron markers (hemoglobin, serum iron, and serum ferritin), cortisol, insulin resistance (measured by HOMA-IR), and the development of GDM in pregnant women between 24 and 28 weeks of gestation. Methods: This analytical cross-sectional study included 150 pregnant women, divided into GDM and non-GDM groups based on oral glucose tolerance test results. Iron markers, cortisol, fasting plasma glucose, insulin, and HOMA-IR levels were measured. Data analysis was performed using SPSS version 25, employing descriptive and inferential statistics, including the Mann-Whitney U test for non-normally distributed variables. Results: Women with GDM exhibited significantly higher mean levels of hemoglobin (12.41±0.68 g/dl), serum iron (19.57±1.14 μmol/L), serum ferritin (25.92±2.58 ng/ml), cortisol (451.32±25.20 nmol/L), fasting plasma glucose (6.83±0.46 mmol/L), insulin (12.50±1.72 μU/mL), and HOMA-IR (3.83±0.78) compared to non-GDM participants (p<0.001 for all comparisons). These findings underscore the pronounced biochemical and hormonal alterations associated with GDM. Conclusion: The study identified a significant association between elevated levels of iron markers, cortisol, and insulin resistance with the development of GDM. These findings suggest that monitoring these parameters during pregnancy could be crucial for the early detection and management of GDM. Individualized iron supplementation strategies may be necessary to prevent iron overload and minimize GDM risk.

Prevalence of gestational diabetes mellitus in Pakistan: a systematic review and meta-analysis

BackgroundA variety of screening tools and criteria are used for the diagnosis of gestational diabetes mellitus (GDM). As a result, the prevalence rate of GDM varied from 4.41% to 57.90% among studies from Pakistan. Beside this disagreement, similar multi-centric studies, community surveys and pooled evidence were lacking from the country. Therefore, this first systematic review and meta-analysis aimed to measure the overall and subgroup pooled estimates of GDM and explore the methodological variations among studies for any inconsistency.MethodsUsing the PRISMA guidelines, seventy studies were identified from PubMed, ScienceDirect, Google Scholar and PakMediNet database. Of them, twenty-four relevant studies were considered for systematic review and nine eligible studies selected for meta-analysis. AXIS was used for measuring quality of reporting, I^2 statistics for heterogeneity among studies and subgroups, funnel plot for reporting potential publication bias and forest plot for presenting pooled estimates.ResultsThe pooled sample of nine studies was 27,034 (126 – 12,450) pregnant women, of any gestational age, from all four provinces of Pakistan. Overall pooled estimate of GDM was 16.7% (95% CI 13.1 – 21.1). The highest subgroup pooled estimate of GDM observed in studies from Balochistan (35.8%), followed by Islamabad (23.9%), Khyber Pakhtunkhwa (17.2%), Sindh (13.2%), and Punjab (11.4%). The studies that adopted 75g 2-h OGTT had a little lower pooled estimate (16.3% vs. 17.3%); and that adopted diagnostic cut-off values [≥ 92 (F), ≥ 180 (1-h) and ≥ 153 (2-h)] had a greater pooled estimate (25.4% vs. 15.8%). The studies that adopted Carpenter criteria demonstrated the highest subgroup pooled estimate of GDM (26.3%), after that IADPSG criteria (25.4%), and ADA criteria (23.9%).ConclusionsAlong with poor quality of reporting, publishing in non-indexed journals and significant disagreement between studies, the prevalence rate of GDM is high in Pakistan. Consensus building among stakeholders for recommended screening methods; and continuous medical education of the physicians are much needed for a timely detection and treatment of GDM.

A Critical Review of Diagnostic Strategies and Maternal Offspring Complications in Gestational Diabetes Mellitus.

Gestational diabetes mellitus (GDM) is a complex and significant health concern affecting pregnant individuals and their offspring. This review provides a comprehensive examination of GDM, focusing on diagnostic strategies and the associated maternal and offspring complications. We delve into the challenges and controversies surrounding GDM diagnosis, including the variability in diagnostic criteria, diagnostic accuracy and reproducibility issues, ethical considerations, and the influence of ethnicity and genetics. Maternal complications, such as preeclampsia, cesarean sections, long-term health implications, and neonatal complications like macrosomia, hypoglycemia, and respiratory distress syndrome, are explored in detail. Additionally, we investigate the long-term risks of childhood obesity and type 2 diabetes in offspring and potential cognitive and developmental outcomes. This review underscores the critical importance of early detection and effective management of GDM, the need for standardized diagnostic criteria, personalized care plans, and the ongoing pursuit of research to enhance our understanding of this complex condition. GDM remains a dynamic field where ongoing innovation and research promise to improve the health outcomes of pregnant individuals and their children.

Performance of glycated hemoglobin A1c for the diagnosis of gestational diabetes mellitus during the SARS-CoV-2 pandemic in Belgium (2020–2021)

ObjectivesDuring the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, health care access was restricted. To reduce the risk of maternal SARS-CoV-2 infection, simplified screening recommendations for gestational diabetes mellitus (GDM) have been suggested, leading to glycated hemoglobin A1c (HbA1c) being proposed as an alternative to the oral glucose tolerance test (OGTT). This study aimed to assess the optimal HbA1c cutoff to confirm GDM diagnosis according to IADPSG/WHO2013 guidelines. MethodsIn this retrospective study, 3361 pregnancies were followed at the hospital of Mouscron and the Cliniques Universitaires St Luc in Brussels (2020–2021). GDM was universally screened in the third trimester of gestation. The ROC curve was used to evaluate the diagnostic performance of HbA1c with OGTT as the reference. Sensitivity, specificity and likelihood ratios for different HbA1c thresholds were calculated. ResultsIn total, 312 women were selected due to HbA1c analysis in addition to OGTT, and 149 had GDM. The area under the ROC curve for GDM detection by HbA1c was 0.73 (95% CI 0.68–0.79, p < 0.0001). The cutoff value chosen as a possible threshold was HbA1c 5.5% (37 mmol/mol). The sensitivity, specificity, positive and negative likelihood ratios for this cutoff were 12.0%, 99.4%, 20 and 0.88, respectively. The Fagan nomogram test showed a posttest GDM probability of approximately 70%, corresponding to a 10-fold higher pretest probability. An HbA1c ≥ 5.5% (37 mmol/mol) would have avoided OGTT in 18% of women with GDM. These women with an HbA1c ≥ 5.5% had significantly higher rates of family history of diabetes, older age, higher BMI and higher blood glucose levels (fasting, 1 h and 2 h) at OGTT. ConclusionOur results are consistent with the literature concerning the diagnostic ability of GDM through HbA1c ≥ 5.5%.

Statistical analysis plan and protocol updates for Gestational diabetes in Uganda and India: Design and Evaluation of Educational Films for Improving Screening and Self-management (GUIDES) trial

BackgroundTimely detection and management of gestational diabetes mellitus (GDM) have been identified as a high priority for policymakers in low- and middle-income countries (LMICs). The GUIDES trial will evaluate a package of three interconnected film-based interventions aimed at improving the timely detection and management of GDM. The protocol for this trial has previously been published; this publication outlines the statistical analysis plan for the trial.Methods and designThe GUIDES study is a multi-country cluster-randomised controlled trial consisting of one trial conducted in Uganda and one in India (30 clusters in each country). Mixed effects models will be used to compare the primary study outcomes of the proportion of women who are tested for GDM between 24 and 32 weeks of pregnancy and the mean fasting blood sugar of women with GDM at 34-week follow-up while accounting for clustering. Secondary analyses will compare the proportion of women with self-reported GDM diagnosis at 32 weeks of pregnancy and the proportion of women with adverse perinatal outcomes related to GDM up to 4 weeks after birth in each trial arm.Trial status and discussionFollow-up is expected to end in March 2023 in Uganda and in May 2023 in India. Analyses will be carried out following this statistical analysis plan in the month following trial completion.Trial registrationClinicalTrials.gov NCT03937050. Registered on 3 May 2019. Clinical Trials Registry India CTRI/2020/02/023605. Registered on 26 February 2020.