Detection Of Gestational Diabetes Mellitus Research Articles

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.
 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.
 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).
 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%.
 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 & 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.
 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.
 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.
 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

Evaluation of First Trimester Fasting Blood Glucose, HOMA-IR and HbA1c in Prediction of Gestational Diabetes Mellitus in Non-Obese Pregnant Women: A Retrospective Study

Aim: Gestational Diabetes Mellitus (GDM) is the most common endocrine complication in pregnancy with fetomaternal comorbidities. It is aimed to evaluate fasting blood glucose (FBG), homeostasis model assessment of insulin resistance (HOMA-IR), and hemoglobin A1c (HbA1c) values in the first trimester in non-obese pregnant women for early detection of GDM.
 Material and Methods: Pregnant women with first-trimester FBG, HOMA-IR, HbA1c values, and second-trimester OGTT results were scanned from the hospital database. First-trimester height, weight, age and gestational weeks were also recorded. The presence of Body Mass Index (BMI) over 30 kg/m2, chronic and systemic disease and history of G(DM) were not included in the study. ROC analysis was performed on FBG, HOMA-IR, and HbA1c.
 Results: This retrospective study was conducted between 01/01/2021, and 01/01/2022 in Tuzla State Hospital Gynecology and Obstetrics Clinic. 131 pregnant women who met the inclusion criteria were reached. Twenty of pregnant women were evaluated as GDM positive(+). Age, height, weight, BMI, gestational week, and nulliparity were observed to be similar between the groups. FBG, insulin, HOMA-IR, and HbA1c values were higher in the group with GDM. As a result of ROC analysis, those with an FBG value of 88,5 mg/dl (sensitivity 68.2%, specificity 68.2%), a HOMA-IR value of 2.24 (sensitivity 63.6%, specificity 64.5%), and a HbA1c (sensitivity 68.2%, specificity 66.7%) value of over 5.25% were observed to be at risk for GDM.
 Conclusion: It has shown that pregnant women with high first trimester FBG, HOMA-IR and HbA1c have a high risk for GDM and can be used as a predictor of GDM.

First-trimester glycaemic markers as predictors of gestational diabetes and its associated adverse outcomes: A prospective cohort study.

Gestational diabetes mellitus (GDM) is associated with excessive fetal growth in later gestation. Recent data suggest accelerated growth may begin before 28 weeks' gestation when GDM is typically diagnosed. The identification of pregnancies at risk of early fetal growth would enable early intervention. We assessed the use of early pregnancy HbA1c in predicting excessive fetal growth. Women were recruited at antenatal booking from a major maternity unit in the UK. HbA1c was measured at <14 weeks gestation in 1243 women at risk of GDM as defined by UK NICE risk factors of whom 1115 underwent OGTT. Women with previous GDM were excluded. Comprehensive fetal ultrasound was performed at 28 weeks' gestation alongside 75 g OGTT in 976 of these women. GDM was defined using WHO criteria. Pregnancy outcomes were extracted from the regional maternity care database. Two hundred and thirty-six women screened positive for GDM. At diagnosis, GDM pregnancies demonstrated higher adjusted fetal weight percentile than non-GDM pregnancies: (51.8 vs. 46.3, p=0.008). This was driven by increases in the fetal abdominal circumference percentile in GDM compared with non-GDM pregnancies (55.3 vs. 46.2, p=<0.001). Early pregnancy HbA1c was higher in the GDM versus non-GDM group: (35.7 mmol/mol vs. 32.9 mmol/mol p=<0.01). A threshold for predicting excessive fetal growth was not identified in this cohort. Accelerated fetal growth is evident prior to the diagnosis of GDM. There remains a need for suitable methods of early identification of pregnancies at high risk for early accelerated fetal growth due to GDM. First-trimester HbA1c was not useful in identifying these pregnancies. WHAT IS ALREADY KNOWN?: Recent research suggests excessive growth associated with GDM may begin prior to 28 weeks' gestation, when GDM is typically tested for WHAT THIS STUDY HAS FOUND?: Pregnancies affected by GDM are already subject to accelerated fetal growth in comparison to non-GDM pregnancies by way of higher estimated fetal weight and fetal abdominal circumference Neither first-trimester HbA1c nor plasma glucose was useful predictors of these outcomes WHAT ARE THE IMPLICATIONS OF THIS STUDY?: Highlights the emergence of excessive growth prior to detection of GDM Reinforces need for suitable methods of identifying such pregnancies in earlier gestation.

Association of interpregnancy interval and gestational diabetes mellitus

Objective To evaluate the association between interpregnancy interval (IPI) and risk for gestational diabetes mellitus (GDM). Methods We conducted a retrospective cohort study among singleton, non-anomalous, live birth pregnancies of 5,705,812 pregnant individuals in the United States from 2016 to 2018. We examined IPI of 4–<6 months, 6–11 months, 12–17 months, 24–35 months, 36–47 months, 48–59 months, 60–71 months, and ≥72 months in comparison to the reference interval of 18–23 months in relation to risk for GDM. We used logistic regression to evaluate the association between IPI and risk for GDM. Results There is a significantly increased risk for GDM associated with IPIs of 6–11 months and 12–17 months compared to the reference of 18–23 months (adjusted Odds Ratio [aOR] 1.05, 95% CI: 1.03–1.07; aOR 1.02, 95% CI: 1.01–1.03). The risk for GDM is greater for longer IPIs (36–47 months aOR 1.10, 95% CI: 1.05–1.08; 48–59 months aOR 1.11, 95% CI: 1.09–1.13; 60–71 months aOR 1.14, 95% CI: 1.12–1.16; ≥72 months aOR 1.31, 95% CI: 1.30–1.33). Conclusion Our findings support the growing evidence that shorter and longer IPI increase the risk of GDM in pregnant individuals. Screening guidelines for detection of GDM may need to be re-evaluated and updated to include longer IPIs (≥36 months) as a risk factor for earlier screening prior to current recommendation of 24 weeks gestational age.

Association of Early Pregnancy Free and Total Triiodothyronine With the Subsequent Risk of Gestational Diabetes Mellitus

ObjectiveTo estimate the association of free triiodothyronine (FT3) and total triiodothyronine (TT3) in early pregnancy and subsequent gestational diabetes mellitus (GDM) risk and define appropriate TT3 thresholds for GDM screening. MethodsThis investigation is a hospital-based cohort study of pregnant women submitted to a universal thyroid function test before 24 weeks of gestation. GDM was diagnosed according to a 75-g oral glucose tolerance test. The association of maternal high FT3 and TT3 levels in early pregnancy with the risk of GDM was estimated using logistic regression. The potential nonlinear association was probed by the restricted cubic spline curve method. ResultsA total of 27 184 pregnant women and 3073 GDM cases were included in the analysis. FT3 and TT3 were associated with an increased subsequent risk of GDM in a nonlinear fashion. The adjusted odds ratios were 1.59 (95% confidence interval, 1.50-1.68) and 2.80 (95% confidence interval, 2.46-3.18) for FT3 and TT3 continuous levels, respectively. Associations were strong in euthyroid women, showed heterogeneity in women with mild thyroid dysfunction, and lacked in patients with overt hypothyroidism and hyperthyroidism. The TT3 thresholds of 1.5 and 2.0 ng/mL between 7 and 12 weeks of gestation and 1.6 and 2.1 ng/mL for 13 to 23 weeks of gestation effectively distinguished the subsequent risk of GDM. ConclusionThe increased FT3 and TT3 levels in early pregnancy were associated with a subsequent higher risk of GDM. These findings provide measures for early detection and potential prevention of GDM.

Perspectives from metabolomics in the early diagnosis and prognosis of gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnant women. The early detection of GDM provides an opportunity for the effective treatment of hyperglycemia in pregnancy, thus decreasing the risk of adverse perinatal outcomes for mothers and newborns. Metabolomics, an emerging technique, offers a novel point of view in understanding the onset and development of diseases and has been repeatedly used in various gestational periods in recent studies of GDM. Moreover, metabolomics provides varied opportunities in the different diagnoses of GDM from prediabetes or predisposition to diabetes, the diagnosis of GDM at a gestational age several weeks earlier than that used in the traditional method, and the assessment of prognosis considering the physiologic subtypes of GDM and clinical indexes. Longitudinal metabolomics truly facilitates the dynamic monitoring of metabolic alterations over the course of pregnancy. Herein, we review recent advancements in metabolomics and summarize evidence from studies on the application of metabolomics in GDM, highlighting the aspects of the diagnosis and differential diagnoses of GDM in an early stage. We also discuss future study directions concerning the physiologic subtypes, prognosis, and limitations of metabolomics.

The diagnostic model for early detection of gestational diabetes mellitus and gestational diabetic nephropathy.

BackgroundGestational diabetes mellitus (GDM) and gestational diabetic nephropathy (GDN) have become an increasingly serious problem worldwide, which can cause a large number of adverse pregnancy consequences for mothers and infants. However, the diagnosis of GDM and GDN remains a challenge due to the lack of optimal biomarkers, and the examination has high requirements for patient compliance. We aimed to establish a simple early diagnostic model for GDM and GDN.MethodsWe recruited 50 healthy pregnant (HP), 99 GDM patients, 99 GDN patients at Daping Hospital. Renal function indicators and blood cell indicators were collected for all patients.ResultsCompared with HP, GDM, and GDN patients exhibited significantly higher urea/creatinine ratio and NEU. The diagnostic model1 based on the combination of urea/creatinine ratio and NEU was built using logistic regression. Based on receiver operating characteristic curve analysis, the area under the curve (AUC) of the diagnostic model was 0.77 (0.7, 0.84) in distinguishing GDM from HP, and the AUC of the diagnostic model was 0.94 (0.9, 0.97) in distinguishing GDN from HP. Meanwhile, the diagnostic model2 based on the combination of β2‐mG, PLT, and NEU in GDM and GDN patients was built using logistic regression, and the area under the ROC curve (AUC ROC) was 0.79 (0.73, 0.85), which was larger than the individual biomarker AUC.ConclusionOur study demonstrated that the diagnostic model established by the combination of renal function indicators and blood cell indicators could facilitate the differential diagnosis of GDM and GDN patients.

Visfatin as a Biomarker for Early Detection of Gestational Diabetes Mellitus

Objective: The current study aims to determine whether an increase in visfatin level resulted in Gestational Diabetes Mellitus (GDM) in pregnant females. Study Design: Retrospective Study Place and Duration: Department of Medicine, Dr. Ziauddin Hospital, North Nazimabad, Karachi, From November 2021 to April 2022. Methods: In this study, 83 pregnant females aged 18-46 years were inducated. After obtaining informed written consent, detailed demographic data were recorded. Gestational age and gravidity among all females were recorded. Previous history and frequency of GDM were assessed. Blood samples were taken to determine the concentration of visfatin and glucose levels. SPSS 24.0 was used to analyze all data. Results: In this study, the mean age was 29.11±8.42 years, and the mean BMI was 26.2±3.32 kg/m2. The mean gestational age was 34.11±4.33 weeks. Most of the cases were from rural areas and had poor socio-economic status. We found that 44 (53.01%) subjects had gestational diabetes mellitus while 39 (46.9%) had no diabetes. The visfatin level in patients with gestational diabetes was higher, 4.2±7.124, compared to non-diabetes cases, 1.4±1.432, with a p-value&lt;0.003. Conclusion: Ultimately, we determined that visfatin levels were considerably elevated in gestational diabetes mellitus beginning in the first trimester of pregnancy. An innovative biomarker for GDM detection is increased visfatin levels during pregnancy. Keywords: Visfatin, Gestational Diabetes, Pregnant Females

Systematic Literature Review: Metode Deteksi Dini Diabetes Mellitus Gestasional

Gestational Diabetes Mellitus (GDM) is one of the medical complications of pregnancy that can cause short-term and long-term risks for the mother and fetus if not detected early and treated appropriately. This study aims to find, select, assess, extract, and synthesize data from several articles on the method of early detection of GDM that can be carried out by health workers. This study uses a systematic literature review method with the PICO framework. Search articles using PubMed and Science Direct databases with keywords and Boolean operators in this study are "early detection" or "screening" and "gestational diabetes mellitus" and "pregnant woman" or “pregnant mother”. Research articles published in 2016–2021, in English, research design: using a quantitative research design. The results of the study found that early detection of GDM can be done through a scoring system, identification of women's diet before pregnancy and optimization of early GDM screening before 24 weeks, monitoring growth and improving early detection services for gestational diabetes in basic health facilities. Health workers need to provide education to women of reproductive age about GDM, so that when they decide to get pregnant they can understand the importance of maintaining blood sugar levels.

Obstetric ultrasonography as a screening tool for the diagnosis of GDM: Detection of raised AFI and large for gestational age fetus

Objective: To establish the role of determining increased amniotic fluid index (AFI) and large for gestational age (LGA) fetus on ultrasound for the screening of gestational diabetes mellitus (GDM). Study Design: Descriptive Observational study. Place and Duration: Department of Gynaecology and Obstetrics, Pakistan Railway Hospital, Rawalpindi from 1st September 2021 till 28th February 2022. Methodology: The pregnant women, between 20-30 weeks of gestation, suspected of having excessive AFI or LGA fetuses on routine clinical examination, were advised to have an obstetric ultrasound scan. AFI &gt; 20 cm or LGA on ultrasound (&gt;90th percentile for that gestation) were considered abnormal. These participants were then subjected to 75-gram Oral Glucose Tolerance test (OGTT). Based on OGTT, the sample population was divided into two groups at the post-stratification stage: with and without Gestational Diabetes Mellitus (GDM). Chi-square test was applied to compare the frequency of increased AFI and LGA between patients having GDM or not having GDM. Results: The study population (n=75) showed the frequency of LGA as 37.3 % and increased AFI as 65.3%. Women with increased AFI were at a significantly greater risk of GDM (65%), as compared to those with normal AFI (27%) with p-value of 0.002. Also, patients having LGA fetuses (60.7%) were at a greater risk of having GDM, as compared to those with normal weight fetuses (34%) with p-value of 0.024. Conclusion: Women with Increased AFI and LGA fetuses between 20-30 weeks of gestation are at a greater risk to develop GDM. Hence these ultrasound parameters can be used as reliable screening tools for the detection of GDM. Keywords: Adverse pregnancy outcome, Fetal macrosomia, Gestational diabetes mellitus, Increased Fetal weight, Polyhydramnios