Detection Of Gestational Diabetes Mellitus Research Articles

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.

Validity of Alternative Claims-based Algorithms for Type 1, Type 2, and Gestational Diabetes in Pregnancy

ObjectiveOur aim in this study was to evaluate the accuracy of alternative algorithms for identifying pre-existing type 1 or 2 diabetes (T1DM or T2DM) and gestational diabetes mellitus (GDM) in pregnant women. MethodsData from a clinical registry of pregnant women presenting to an Edmonton diabetes clinic between 2002 and 2009 were linked and administrative health records. Three algorithms for identifying women with T1DM, T2DM, and GDM based on International Classification of Diseases---tenth revision (ICD-10) codes were assessed: delivery hospitalization records (Algorithm #1), outpatient clinics during pregnancy (Algorithm #2), and delivery hospitalization plus outpatient clinics during pregnancy (Algorithm #3). In a subset of women with clinic visits between 2005 and 2009, we examined the performance of an additional Algorithm #4 based on Algorithm #3 plus outpatient clinics in the 2 years before pregnancy. Using the diabetes clinical registry as the “gold standard,” we calculated true positive rates and agreement levels for the algorithms. ResultsThe clinical registry included data on 928 pregnancies, of which 90 were T1DM, 89 were T2DM, and 749 were GDM. Algorithm #3 had the highest true positive rate for the detection of T1DM, T2DM, and GDM of 94%, 72%, and 99.9%, respectively, resulting in an overall agreement of 97% in diagnosis between the administrative databases and the clinical registry. Algorithm #4 did not provide much improvement over Algorithm #3 in overall agreement. ConclusionsAn algorithm based on ICD-10 codes in the delivery hospitalization and outpatient clinic records during pregnancy can be used to accurately identify women with T1DM, T2DM, and GDM.

Diagnosis and management of gestational diabetes mellitus

Background: Gestational diabetes mellitus (GDM) is a condition characterized by hyperglycemia that is first diagnosed during pregnancy. It increases both fetal and maternal risk and is related to future metabolic abnormalities in women and their offspring. Therefore, appropriate diagnosis and management of GDM are crucial.Current Concepts: There are two strategies to diagnose GDM—a one-step strategy comprising a 75 g oral glucose tolerance test (OGTT), and a two-step strategy involving a 50 g OGTT followed by a 100 g OGTT. The former can detect more glucose abnormality during pregnancy; however, either method is acceptable, considering the evidence regarding pregnancy outcomes. The mainstay of glucose management in GDM is medical nutrition therapy with appropriate physical activity and self-monitoring of glucose. Insulin therapy is required if hyperglycemia cannot be controlled by lifestyle interventions. Generally, insulin therapy is immediately discontinued after childbirth, and 75 g OGTT is recommended at 4 to 12 weeks in the postpartum period and every 1 to 3 years thereafter. Lifestyle intervention is important in women with a history of GDM to prevent future diabetes.Discussion and Conclusion: GDM prevalence is increasing because of higher maternal age and prevalent obesity. Healthcare providers and women of childbearing age need to be more careful with regard to the detection and management of GDM, and treatment strategies should be personalized.

MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women

Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who participated in the multicenter prospective cohort study ‘Cuido mi embarazo’. A machine-learning-driven method was used to select the best predictive variables for GDM risk: age, family history of type 2 diabetes, previous diagnosis of hypertension, pregestational body mass index, gestational week, parity, birth weight of last child, and random capillary glucose. An artificial neural network approach was then used to build the model, which achieved a high level of accuracy (70.3%) and sensitivity (83.3%) for identifying women at high risk of developing GDM. This AI-based model will be applied throughout Mexico to improve the timing and quality of GDM interventions. Given the ease of obtaining the model variables, this model is expected to be clinically strategic, allowing prioritization of preventative treatment and promising a paradigm shift in prevention and primary healthcare during pregnancy. This AI model uses variables that are easily collected to identify pregnant women at risk of developing GDM with a high level of accuracy and precision.

Study on the predictive value of serum hypersensitive C-reactive protein, homocysteine, fibrinogen, and omentin-1 levels with gestational diabetes mellitus

Objective: To investigate whether hypersensitive C-reactive protein (Hs-CRP), homocysteine, fibrinogen, and omentin-1 could predict gestational diabetes mellitus (GDM) risk. Methods: Case–control study was conducted at Hengshui People’s Hospital. The GDM group included data about 150 patients aged between 22 and 35 years in 24–28 weeks. An equivalent comparative control group without GDM was composed of the same pool of patients. Body mass index (BMI), total cholesterol (TC), triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), oral glucose tolerance test (OGTT) 0–2h, hs-CRP, homocysteine, fibrinogen, and omentin-1 levels were studied in the serum samples of research groups. Univariate logistic regression analysis was used to explore the risk factors of GDM. The area under the curve (AUC) was calculated by the receiver operating characteristic curve (ROC) to analyze the predictive values. Results: Hs-CRP, homocysteine, and fibrinogen in GDM group were significantly higher than those in non-GDM group. Omentin-1 were significantly lower than those in non-GDM group. Logistic regression showed that hs-CRP, homocysteine, fibrinogen, and omentin-1 were risk factors for GDM. The AUC of the established GDM risk prediction model was 0.977, and the sensitivity and specificity were 92.10% and 98.70%, respectively; which were greater than that of hs-CRP, homocysteine, fibrinogen, and omentin-1 alone. Conclusions: Hs-CRP, homocysteine, fibrinogen, and omentin-1 in pregnancy have important clinical value for the prediction of GDM. We used these laboratory indications to establish a GDM risk prediction model that allows for early detection and treatment of GDM, lowering the morbidity of maternal and infant complications.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers.

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

The relationship between the two endocrine pathologies: subclinical gestational hypothyroidism and gestational diabetes mellitus

BACKGROUND: Over the past decade, gestational diabetes mellitus has become of increasing medical and social importance. It happens due to its increased prevalence and due to its negative impact on pregnancy and long-term metabolic disorders in the mother and fetus.&#x0D; AIM: The aim of this study was to assess the relationship between subclinical gestational hypothyroidism and gestational diabetes mellitus, the two most common endocrine pathologies in pregnancy.&#x0D; MATERIALS AND METHODS: We studied 200 medical records of pregnant women. The main group included 133 patients with subclinical gestational hypothyroidism, and the control group consisted of 67 women without endocrine pathology. The diagnosis of gestational diabetes mellitus was made based on the Ministry of Health of the Russian Federation clinical guidelines criteria. The diagnosis of subclinical hypothyroidism was made based on the thyroid-stimulating hormone level above 2.5 IU/ml in combination with an increased titer of antithyroid antibodies or above 4.0 IU/ml in the absence of any thyroid disorder. Statistical analysis was carried out using the StatTech v.2.1.0 program (Stattech Ltd, Russia).&#x0D; RESULTS: The prevalence of hypothyroidism was higher among women with family history of diabetes mellitus. The chances of developing gestational diabetes mellitus increased by 9.706 times in the presence of hypothyroidism, by 1.077 times with an increase in age by one full year at the time of seeing the doctor, and by 1.023 times with an increase in weight before pregnancy by one kilogram. The thyroid-stimulating hormone level of more than 2.7 IU/ml predicted the development of gestational diabetes mellitus with a sensitivity of 71.4% and a specificity of 63.1%.&#x0D; CONCLUSIONS: Subclinical gestational hypothyroidism and gestational diabetes mellitus are interrelated endocrine disorders with common pathophysiological predictors. Among women with a normal body mass index, subclinical gestational hypothyroidism is a more significant risk factor for gestational diabetes mellitus than an increase in age or body weight. A certain threshold level of thyroid-stimulating hormone (more than 2.7 IU/ml) in the first trimester increases the chances of developing gestational diabetes mellitus and should be considered as a signal for timely prevention and detection of gestational diabetes mellitus.

Risk factor-based screening compared to universal screening for gestational diabetes mellitus in marginalized Burman and Karen populations on the Thailand-Myanmar border: An observational cohort.

Background: Gestational diabetes mellitus (GDM) contributes to maternal and neonatal morbidity. As data from marginalized populations remains scarce, this study compares risk-factor-based to universal GDM screening in a low resource setting. Methods: This is a secondary analysis of data from a prospective preterm birth cohort. Pregnant women were enrolled in the first trimester and completed a 75g oral glucose tolerance test (OGTT) at 24-32 weeks' gestation. To define GDM cases, Hyperglycaemia and Adverse Pregnancy Outcomes (HAPO trial) criteria were used. All GDM positive cases were treated. Sensitivity and specificity of risk-factor-based selection for screening (criteria: age ≥30y, obesity (Body mass index (BMI) ≥27.5kg/m 2), previous GDM, 1 st degree relative with diabetes, previous macrosomia (≥4kg), previous stillbirth, or symphysis-fundal height ≥90th percentile) was compared to universal screening using the OGTT as the gold standard. Adverse maternal and neonatal outcomes were compared by GDM status. Results: GDM prevalence was 13.4% (50/374) (95% CI: 10.3-17.2). Three quarters of women had at least one risk factor (n=271 women), with 37/50 OGTT positive cases correctly identified: sensitivity 74.0% (59.7-85.4) and specificity 27.8% (3.0-33.0). Burman women (self-identified) accounted for 29.1% of the cohort population, but 38.0% of GDM cases. Percentiles for birthweight (p=0.004), head circumference (p=0.002), and weight-length ratio (p=0.030) were higher in newborns of GDM positive compared with non-GDM mothers. 21.7% (75/346) of newborns in the cohort were small-for-gestational age (≤10 th percentile). In Burman women, overweight/obese BMI was associated with a significantly increased adjusted odds ratio 5.03 (95% CI: 1.43-17.64) for GDM compared with normal weight, whereas in Karen women, the trend in association was similar but not significant (OR 2.36; 95% CI 0.95-5.89). Conclusions: Risk-factor-based screening missed one in four GDM positive women. Considering the benefits of early detection of GDM and the limited additional cost of universal screening, a two-step screening program was implemented.

Study of altered gut microbial dynamics and their association with gestational diabetes mellitus

The human gastrointestinal system is home to billions of different microbial species that coexist peacefully with the host. Age, diet, and environment affect gut microbial structure and diversity. Bacteroidetes and Firmicutes are the main gut microbial groups in humans. Human physiology is greatly affected by gut microbial metabolism and host–microbe interactions. Gestational diabetes mellitus (GDM) women have a different gut microbial makeup. However, whether these microbial changes in the stomach may be detected before the diagnosis of GDM remains unknown. This study aims to study the dynamics of gut microbe in healthy and GDM women and analyze the potential biomarkers for early detection of GDM in pregnant women. In this study, gut microbial variations have been characterized and their relationship is evaluated with GDM development. A large multi-taxon microbial shift between GDM and normal cases was evident, indicating that few microbial disruptions from early to mid-pregnancy were related to the later development of GDM. Results of linear discriminant analysis (LDA) show the variable relative abundance of 13 taxonomic groups (at the family level) between the GDM and normal groups. Moreover, 8 taxonomic groups (at the family level) were found to be present in the GDM group only. The results contribute to the gut microbiota’s potential relevance as a biomarker for the early detection of GDM.

PREDICTIVE VALUE OF NEUTROPHIL TO LYMPHOCYTE RATIO (NLR) IN EARLY DETECTION OF GESTATIONAL DIABETES MELLITUS (GDM) AMONG PREGNANT WOMEN IN FIRST TRIMESTER OF PREGNANCY

Background &amp; Objective: A substantial number of pregnant women develop gestational diabetes mellitus (GDM). Various biomarkers are being used to diagnose gestational diabetes mellitus. Early detection of GDM can done by using inflammatory markers like neutrophil-to-lymphocyte ratio (NLR) and the platelet- to-lymphocyte ratio (PLR) that are rapid, inexpensive and easy to use in different clinical setting. The objective of this study was to evaluate diagnostic accuracy of raised neutrophil to lymphocyte ratio for detection of gestational diabetes mellitus taking OGTT as gold standard.&#x0D; Methods: This was a cross-sectional study held in Department of Obstetrics and Gynecology CMH Lahore from November, 2018 –May, 2020. After approval from hospital ethical review committee and taking an informed consent, 473 pregnant women enrolled in first trimester between age 18-35 years, fulfilling the inclusion criteria were included whereas known diabetics, patients with cardiovascular disorders, previous history of GDM were excluded. Blood samples in sterilized manner were drawn and sent to hospital laboratory for analysis of NLR. Then they were screened during 24-28 weeks of gestation for diagnosis of gestational diabetes mellitus on OGGT. The females were labelled for positive NLR and GDM as per operational definition. Diagnostic accuracy of NLR was calculated for development of gestational diabetes mellitus.&#x0D; Results: Mean age of females was 26.72 ± 4.90 years and mean gestational age was 15.50 ± 1.67 weeks. 94(19.9%) females who had raised neutrophil to lymphocyte ratio while 397(80.1%) females had normal neutrophil to lymphocyte ratio and there were 90(19%) females who had gestational diabetes mellitus while rests of 383(81%) did not have gestational diabetes mellitus. The sensitivity, specificity, positive predictive value, negative predictive value and overall diagnostic accuracy of NRL was 95.56%, 97.91%, 91.49%, 98.94% and 97.46%, respectively.&#x0D; Conclusion: The study concluded that raised neutrophil to lymphocyte ratio is significantly high among gestational diabetes mellitus and NLR has a high diagnostic accuracy for predicting gestational diabetes mellitus.

GDM-PREP: A Rule-Based Technique to Enhance Early Detection of Gestational Diabetes Mellitus

GDM-PREP: A Rule-Based Technique to Enhance Early Detection of Gestational Diabetes Mellitus