Prediction Of Gestational Diabetes Mellitus Research Articles

A first trimester prediction model and nomogram for gestational diabetes mellitus based on maternal clinical risk factors in a resource-poor setting

BackgroundThe implementation of universal screening for Gestational Diabetes Mellitus (GDM) is challenged by several factors key amongst which is limited resources, hence the continued reliance on risk factor-based screening. Effective identification of high-risk women early in pregnancy may enable preventive intervention. This study aimed at developing a GDM prediction model based on maternal clinical risk factors that are easily assessable in the first trimester of pregnancy in a population of Nigerian women.MethodsThis was a multi-hospital prospective observational cohort study of 253 consecutively selected pregnant women from which maternal clinical data was collected at 8–12 weeks gestational age. Diagnosis of GDM was made via a one-step 75-gram Oral Glucose Tolerance Test (OGTT) at 24–28 weeks of gestation. A GDM prediction model and nomogram based on selected maternal clinical risk factors was developed using multiple logistic regression analysis, and its performance was assessed by Receiver Operator Curve (ROC) analysis. Data analysis was carried out using Statistical Package for Social Sciences (SPSS) version 25 and Python programming language (version 3.0).ResultsIncreasing maternal age, higher body mass index (BMI), a family history of diabetes mellitus in first-degree relative and previous history of foetal macrosomia were the major predictors of GDM. The model equation was: LogitP = 6.358 − 0.066 × Age − 0.075 × First trimester BMI − 1.879 × First-degree relative with diabetes mellitus − 0.522 × History of foetal macrosomia. It had an area under the receiver operator characteristic (ROC) curve (AUC) of 0.814 (95% CI: 0.751–0.877; p-value < 0.001), and at a predicted probability threshold of 0.745, it had a sensitivity of 79.2% and specificity of 74.5%.ConclusionThis first trimester prediction model reliably identifies women at high risk for GDM development in the first trimester, and the nomogram enhances its practical applicability, contributing to improved clinical outcomes in the study population.

The application of plasma circRAD18 in the prediction of gestational diabetes mellitus (GDM) and its adverse effects

BackgroundIn cancer biology, circRAD18 promotes glucose metabolism, potentially indicating its involvement in glucose metabolism-related disorders, such as gestational diabetes mellitus (GDM). The present study investigated the predictive role of circRAD18 in GDM and its potential adverse effects.MethodsA total of 482 women who intended to get pregnant in short-term were enrolled. For those who successfully conceived, plasma samples were collected and followed up until delivery to monitor the occurrence of GDM and its associated adverse events. The accumulation of circRAD18 in plasma was analyzed using RT-qPCR. GDM-free curves and ROC curves were plotted to assess the predictive value of plasma circRAD18 for GDM.ResultsAfter admitting 482 female patients, 388 of them achieved pregnancy within half a year. During the follow-up period, 52 cases were diagnosed with GDM. Compared to non-GDM group (n = 336), the GDM group (n = 52) had a lower accumulation level of circRAD18 on the day of pregnancy confirmation. In addition, low levels of circRAD18 accumulation on that day distinguished potential GDM patients from non-GDM cases. The 388 cases were divided into high and low circRAD18 level groups (n = 194). GDM-free curve analysis showed that patients in the low circRAD18 level group had a higher incidence of GDM compared to the high level group (43/194 vs. 9/194). A close association was found between low levels of plasma circRAD18 and hypertension, but not premature delivery, intrauterine death, malformation, intrauterine infection, miscarriage, macrosomia or intrauterine distress.ConclusionThe reduction in the accumulation of plasma circRAD18 is predictive of GDM and hypertension in pregnant women.

Genetic Susceptibility, Mendelian randomization and Nomogram Model Construction of Gestational Diabetes Mellitus

Abstract Context Gestational diabetes mellitus (GDM) is a pregnancy complicated disease that poses a risk to maternal and infant health. However, the etiology of the disease has been not yet elucidated. Objective To detect the genetic susceptibility and construct a nomogram model with significantly associated polymorphisms and key clinical indicators for early prediction of gestational diabetes mellitus (GDM). Methods 11 functional single nucleotide polymorphisms (SNPs) screened by genome-wide association study (GWAS) were genotyped in 554 GDM cases and 641 healthy controls. Functional analysis of GDM positively associated SNPs, Multivariate mendelian randomization (MVMR) and a GDM early predictive nomogram model construction were performed. Result rs1965211, rs3760675 and rs7814359 were significantly associated with genetic susceptibility to GDM after adjusting age and pre-pregnancy BMI (pre-BMI). It seems that GDM associated SNPs have effects on regulating target gene transcription factor binding, post transcriptional splicing, and translation product structure. Besides, rs3760675 can be expression quantitative trait locis (eQTLs) and increase the XAB2 mRNA expression level (P = 0.047). The MVMR analysis showed that the increase of clinical variables of BMI, HbA1c and FPG had significant causal effects on GDM (BMI-ORMVMR = 1.52, HbA1c-ORMVMR = 1.32, FPG-ORMVMR = 1.78), P &amp;lt;0.05. A nomogram model constructed with pre-BMI, FPG, HbA1c, and genotypes of rs1965211, rs3760675 and rs7814359 showed an area under the ROC curve of 0.824. Conclusion Functional polymorphisms can change women's susceptibility to GDM and the predictive nomogram model based on genetic and environmental factors can effectively distinguish individuals with different GDM risks in early stages of pregnancy.

Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors

ObjectiveTo build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.MethodsTo develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.ResultsAn area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).

Prevalence and Predictors of Gestational Diabetes Mellitus in Sub-Saharan Africa: A 10-Year Systematic Review.

Gestational diabetes mellitus (GDM) remains a global public health problem, which affects the well-being of mothers and their children in sub-Saharan Africa (SSA). Studies conducted in different geographical areas provide varied results on its prevalence and predictors. Understanding the extent and predictors of GDM in SSA is important for developing effective interventions and policies. Thus, this review aimed to investigate the prevalence of GDM and its predictive factors in sub-Saharan Africa. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards in this review. An extensive search of the PubMed, Web of Sciences and EMBASE databases was carried out covering papers from 2012 to 2022 to assess the prevalence and predictors of GDM. Microsoft Excel 2019 was utilised for study management. GraphPad Prism Version 8.0 and the MedCalc statistical software were employed for data analysis. The findings were analysed using textual descriptions, tables, forest plots and heat maps. Using 30 studies with 23,760 participants that satisfied the inclusion criteria, the review found the overall prevalence of GDM in SSA to be 3.05% (1.85%-4.54%). History of preterm delivery, alcohol consumption, family history of diabetes, history of stillbirths, history of macrosomia, overweight or obesity and advanced mother age were all significant predictors of gestational diabetes. Additionally, various biomarkers such as haemoglobin, adiponectin, leptin, resistin, visfatin, vitamin D, triglycerides and dietary intake type were identified as significant predictors of GDM. In sub-Saharan Africa, there is a high pooled prevalence of gestational diabetes mellitus. In the light of the predictors of GDM identified in this review, it is strongly recommended to implement early screening for women at risk of developing gestational diabetes during their pregnancy. This proactive approach is essential for enhancing the overall well-being of both mothers and children.

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.

First-Trimester Triglyceride-Glucose Index and Triglyceride/High-Density Lipoprotein Cholesterol are Predictors of Gestational Diabetes Mellitus Among the Four Surrogate Biomarkers of Insulin Resistance.

This study seeks to assess the potential of early pregnancy Triglyceride Glucose Index (TyG), triglyceride to High-Density Lipoprotein Cholesterol ratio (TG/HDL-c), Low-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol ratio (LDL-C/HDL-C), and Total Cholesterol to High-Density Lipoprotein Cholesterol ratio (TC/HDL-C) in predicting Gestational Diabetes Mellitus (GDM). A total of 1073 adults singleton pregnant women were enrolled from June 2017 to September 2019. Complete anthropometric data and lipid profiles were measured in the first trimester (before 12 weeks gestation) and a 75g oral glucose tolerance test (OGTT) at 24-28 weeks was performed. Based on OGTT results, participants were categorised into Normal Glucose Tolerance (NGT) group (n=872) and GDM group (n=201). General data, laboratory test results, and surrogate insulin resistance indicators such as TyG index, TG/HDL-C, LDL-C/HDL-C, and TC/HDL-C were documented and compared. To compare differences between the two groups, t-test was used, Spearman correlation analysis and linear regression analysis were performed to establish associations between these indicators and insulin resistance in GDM. Receiver Operating Characteristic (ROC) curves were generated to compare the thresholds of these indicators for predicting GDM during pregnancy and to quantify overall diagnostic accuracy. Individuals with GDM had higher TyG, TG/HDL-C, and LDL-C/HDL-C levels (P < 0.001), but with no significant difference observed in TC/HDL-C. All four ratios were positively correlated with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), yet only TyG emerged as an independent risk factor for HOMA-IR. The Area under the Curve (AUC) of TyG index (0.692) was comparable to that of HOMA-IR (0.703). The cut-off points for TyG index, TG/HDL-C, and HOMA-IR in predicting GDM were 7.088, 0.831, and 1.8, respectively. HOMA-IR exhibited the highest sensitivity (79.1%), while TyG index (64.3%) and TG/HDL-C ratio (64.3%) demonstrated better specificity compared to HOMA-IR (56.3%). LDL-C/HDL-C and TC/HDL-C offered no discernible predictive advantage. Early pregnancy TyG index and TG/HDL-C can aid in identifying pregnant women at risk for GDM, potentially facilitating early and effective intervention to improve prognosis. TyG index exhibited superior predictive capability compared to TG/HDL-C.

Serum BAFF (B-cell activating factor) and APRIL (a proliferation-inducing ligand) levels in the first trimester may predict the future development of GDM (gestational diabetes mellitus)

BackgroundGestational diabetes mellitus (GDM) is a prevalent condition with an unclear pathogenesis. B-cell activating factor (BAFF) and a proliferation-inducing ligand (APRIL) are potential key players in GDM. Participants, materials, and methodsIn a longitudinal observational study, we monitored women from the first trimester through 24–28 weeks of gestation, focusing on the development of GDM. Serum levels of BAFF and APRIL, as well as their mRNA expression, were evaluated in both the first and third trimesters. Furthermore, we assessed cytokines, adipokines, and placental hormones in the serum. ResultsIn the first trimester, participants who later developed GDM exhibited elevated serum BAFF and reduced serum APRIL levels, although the mRNA expression of these molecules was similar to controls. Serum BAFF exhibited significant positive correlations with metabolic markers and placental hormones. Conversely, serum APRIL was negatively correlated with insulin resistance and inflammatory markers but positively correlated with adiponectin. In the early third trimester, GDM participants continued to display higher serum BAFF levels and lower serum APRIL levels than controls. There was no significant difference in mRNA expression of BAFF between GDM and control groups. Conversely, APRIL mRNA expression was significantly lower in the GDM group. The predictive potential of first-trimester BAFF and APRIL levels for future GDM development was explored, with both molecules demonstrating strong predictive capability. Discussion and conclusionThis study suggests that elevated serum BAFF and reduced serum APRIL levels during pregnancy may be associated with the development of GDM. These biomarkers can serve as potential early predictors for GDM.

Review of Genetic and Artificial Intelligence approaches to improving Gestational Diabetes Mellitus Screening and Diagnosis in sub-Saharan Africa.

Background: Adverse outcomes from gestational diabetes mellitus (GDM) in the mother and newborn are well established. Genetic variants may predict GDM and Artificial Intelligence (AI) can potentially assist with improved screening and early identification in lower resource settings. There is limited information on genetic variants associated with GDM in sub-Saharan Africa and the implementation of AI in GDM screening in sub-Saharan Africa is largely unknown. Methods: We reviewed the literature on what is known about genetic predictors of GDM in sub-Saharan African women. We searched PubMed and Google Scholar for single nucleotide polymorphisms (SNPs) involved in GDM predisposition in a sub-Saharan African population. We report on barriers that limit the implementation of AI that could assist with GDM screening and offer possible solutions. Results: In a Black South African cohort, the minor allele of the SNP rs4581569 existing in the PDX1 gene was significantly associated with GDM. We were not able to find any published literature on the implementation of AI to identify women at risk of GDM before second trimester of pregnancy in sub-Saharan Africa. Barriers to successful integration of AI into healthcare systems are broad but solutions exist. Conclusions: More research is needed to identify SNPs associated with GDM in sub-Saharan Africa. The implementation of AI and its applications in the field of healthcare in the sub-Saharan African region is a significant opportunity to positively impact early identification of GDM.

Triglyceride-glucose index in early pregnancy predicts the risk of gestational diabetes: a prospective cohort study

ObjectiveThis study aimed to investigate the association between the triglyceride-glucose (TyG) index in early pregnancy and the development of gestational diabetes mellitus (GDM) in the second trimester. The primary objectives were to evaluate the predictive potential of the TyG index for GDM, determine the optimal threshold value of the TyG index for GDM assessment, and compare the predictive performance of the TyG index alone versus its combination with maternal age and pre-pregnancy body mass index on GDM. Moreover, the study explored the association between the TyG index in early pregnancy and the risk of other pregnancy-related complications (PRCs), such as placental abruption and gestational hypertension.Patients and methodsThis prospective cohort study recruited 1,624 pregnant women who underwent early pregnancy antenatal counseling and comprehensive assessments with continuous monitoring until delivery. To calculate the TyG index, health indicators, including maternal triglycerides and fasting plasma glucose, were measured in early pregnancy (< 14 weeks of gestation). The predictive power of the TyG index for evaluating GDM in Chinese pregnant women was determined using multifactorial logistic regression to derive the odds ratios and 95% confidence interval (CI). Subgroup analyses were conducted, and the efficacy of the TyG index in predicting PRCs was assessed via receiver operating characteristic (ROC) curve analysis and restricted cubic spline, with the optimal cutoff value calculated.ResultsLogistic regression analyses revealed a 2.10-fold increase in the GDM risk for every 1-unit increase in the TyG index, after adjusting for covariates. The highest GDM risk was observed in the group with the highest TyG index compared with the lowest quintile group (odds ratios: 3.25; 95% CI: 2.23–4.75). Subgroup analyses indicated that exceeding the recommended range of gestational weight gain and an increased GDM risk were significantly associated (P = 0.001). Regarding predictive performance, the TyG index exhibited the highest area under the curve (AUC) value in the ROC curve for GDM (AUC: 0.641, 95% CI: 0.61–0.671). The optimal cutoff value was 8.890, with both sensitivity and specificity of 0.617.The combination of the TyG index, maternal age, and pre-pregnancy body mass index proved to be a superior predictor of GDM than the TyG index alone (AUC: 0.672 vs. 0.641, P < 0.01). After adjusting for multiple factors, the analyses indicated that the TyG index was associated with an increased risk of gestational hypertension. However, no significant association was noted between the TyG index and the risk of preeclampsia, placental abruption, intrauterine distress, or premature rupture of membranes.ConclusionThe TyG index can effectively identify the occurrence of GDM in the second trimester, aligning with previous research. Incorporating the TyG index into routine clinical assessments of maternal health holds significant practical implications. Early identification of high-risk groups enables healthcare providers to implement timely interventions, such as increased monitoring frequency for high-risk pregnant women and personalized nutritional counseling and health education. These measures can help prevent or alleviate potential maternal and infant complications, thereby enhancing the overall health outcomes for both mothers and babies.

Serum proteins differentially expressed in gestational diabetes mellitus assessed using isobaric tag for relative and absolute quantitation proteomics.

As a well-known fact to the public, gestational diabetes mellitus (GDM) could bring serious risks for both pregnant women and infants. During this important investigation into the linkage between GDM patients and their altered expression in the serum, proteomics techniques were deployed to detect the differentially expressed proteins (DEPs) of in the serum of GDM patients to further explore its pathogenesis, and find out possible biomarkers to forecast GDM occurrence. To investigation serum proteins differentially expressed in GDM were assessed using isobaric tag for relative and absolute quantitation (iTRAQ) proteomics and bioinformatics analyses. Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria. Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation, and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry. Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis, and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA). Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDM gravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest 16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteins associated with lipid metabolism, coagulation cascade activation, complement system and inflammatory response in the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serum of GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk of gestation. GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complement system and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.

Gestational diabetes mellitus early prediction by maternal body fat index: A cohort study

ObjectivesTo compare the performance of maternal body fat index (BFI) assessed during the first 20+6 weeks among 138 pregnant women in an ultrasound outpatient clinic as a predictor of gestational diabetes mellitus (GDM) later in pregnancy. MethodMaternal visceral and subcutaneous fat was measured with a convex ultrasound probe placed in two locations on the maternal abdominal surface: the first in the mid-sagittal epigastric region, visualising epigastric fat, and the second 2cm above the maternal umbilical scar, visualising periumbilical fat. Ultrasound callipers measured the distance from dermal edge to the linea alba and after from the linea alba to the anterior hepatic surface (epigastric fat). Periumbilical fat was measured from the dermal edge to the linea alba and after from the linea alba to the anterior aortic surface. The BFI formula was [visceral adipose tissue (mm)×subcutaneous adipose tissue (mm)]/maternal height (cm). ResultsThe best thresholds for predicting GDM outcome for epigastric and periumbilical BFI were 1.2 and 4.8, respectively. Odds ratio, sensitivity and specificity were 5.88 (95% CI 1.86–18.6), 80.9%, 58.0% for the epigastric site and 6.31 (95% CI 1.73–22.94), 84.2%, 54.2% for the periumbilical site. Pre-pregnancy body mass index compatible with adult obesity shows inadequate predictive performance for GDM outcome. Only epigastric BFI above 1.2 maintained statistical significance for GDM in the logistic regression analysis, when compared to periumbilical BFI above 4.8. ConclusionEpigastric BFI above 1.2 during the first half of pregnancy may help identify women at risk of developing GDM later in pregnancy.

Can System Inflammation Response Index or Systemic Immune Inflammation Index predict gestational diabetes mellitus in the first trimester? A prospective observational study.

To evaluate System Inflammation Response Index (SIRI) and Systemic Immune Inflammation Index (SII), which are the inflammatory indices, for the prediction of gestational diabetes mellitus (GDM) in the first trimester. This was a prospective observational study conducted in a tertiary center from April 2023 to September 2023. Ninety-four pregnant women with gestational diabetes and 107 healthy pregnant women were included. The two groups were compared according to first-trimester SIRI and SII values. A receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off levels of SII and SIRI in predicting GDM. Significantly higher first-trimester SII and SIRI values were present in the gestational diabetes group (P < 0.001). Optimal cut-off values in the prediction of gestational diabetes were found to be 1.58 (area under the curve [AUC] 0.71, 67% sensitivity, 65% specificity, 95% confidence interval [CI] 0.64-0.78, P < 0.001) and 875 (AUC 0.70, 66% sensitivity, 65% specificity, 95% CI 0.63-0.77, P < 0.001) for SIRI and SII, respectively. Neutrophil counts, mean platelet volume (MPW), neutrophil to lymphocyte ratio (NLR), and red cell distribution width (RDW) were significantly higher in the GDM group (P < 0.001, P = 0.02, P = 0.01, P < 0.01, respectively). Novel inflammatory indices SII and SIRI may be useful in the prediction of GDM in the first trimester, but their utility in the prediction of insulin requirement is questionable. They may be used as additional tools in routine clinical practice.

Fetal Anterior Abdominal Wall Thickness as an Indicator of Gestational Diabetes: A Sonographic Study

Objective: During pregnancy, sonographic measurements of subcutaneous adipose tissue may be a reliable diagnostic predictor of gestational diabetes mellitus (GDM) and fetal metabolism. A sonographic view of the fetal abdominal circumference can be used to measure the fetal anterior abdominal wall thickness (AAWT), which is straightforward and simple. Consequently, in this study, the aim was to evaluate whether the fetal AAWT value could be used as a diagnostic indicator of GDM, thus preventing the adverse neonatal and maternal outcomes thereof, including shoulder dystocia, neonatal hypoxia, and injury to the birth canal, secondary to fetal macrosomia. Materials and Methods: This prospective cross-sectional study was conducted at a hospital in Lahore, Pakistan, over a 9-month period. A total of 176 female participants in their third trimester of pregnancy were included in this study. The participants were assigned to two cohorts: a cohort of 88 participants diagnosed with GDM and a control cohort of 88 participants without GDM. An independent sample t test, analysis of variance, and chi-square test were used to compare the characteristics between the two cohorts of participants. A final analysis was performed to determine the strength of the correlations between the estimated fetal weight (EFW) and AAWT values. Results: The GDM and control cohorts had mean fetal AAWT values of 12.65 ± 1.276 and 6.389 ± 1.503, respectively. Thus, a statistically significant difference in the fetal AAWT values was observed between the GDM and control cohorts. Furthermore, a statistically significant positive correlation between the EFW and AAWT values was observed ( P = .01). Conclusion: This study highlighted that women with GDM have a considerably higher fetal AAWT values than those without GDM. In addition, the findings revealed that the AAWT value was correlated with the EFW value. Consequently, an increased fetal AAWT value can potentially be utilized as a predictive indicator for those pregnant women at risk of developing GDM, thus preventing adverse maternal and fetal outcomes.

Maternal Visceral Fat in Prediction of Gestational Diabetes Mellitus.

Objective: To determine the diagnostic performance of maternal abdominal visceral adipose tissue thickness, measured by ultrasound, in predicting gestational diabetes mellitus (GDM). Patients and methods: A prospective diagnostic study was conducted on low-risk pregnant women attending our antenatal care clinic. All underwent abdominal visceral adipose tissue (VAT) measurement by two-dimension transabdominal ultrasound twice, at late first trimester (gestational age: GA 11-14 weeks) and second trimester (GA 18-22 weeks). All patients underwent a two-step approach for screening and diagnosis of GDM between GA 24 and 28 weeks. Results: A total of 141 women were recruited into the study; including 32 (22.7%) women with GDM, and 109 (77.3%) women of non-GDM, between GA 24 and 28 weeks. The means VAT at the 1st, 2nd trimester and the difference of VAT of GDM group were 4.0 ± 0.27 cm, 5.7 ± 1.12 cm, and 1.6 ± 0.91 cm respectively. The means VAT at 1st, 2nd trimester and the difference of VAT of non-GDM group were 3.8 ± 1.01 cm, 5.4 ± 1.07 cm, and 1.6 ± 1.12 cm respectively. There were no significant differences of VAT measurements (1st, 2nd and the difference) between both groups. The VAT thickness was slightly greater in the GDM group but the mean differences between 1st and 2nd trimester were comparable between the two groups. The diagnostic performance of VAT, maternal age and body mass index (BMI) in predicting GDM was comparable. Conclusion: Measurement of maternal visceral adipose thickness in early pregnancy is not effective in predicting GDM among Thai women, which is different from most studies conducted on western women. However, a trend of higher VAT in the GDM group was noted.

LncRNA HCP5 is Highly Expressed in Gestational Diabetes Mellitus to Suppress Insulin Secretion.

LncRNA HCP5 has been reported to participate in high glucose-induced pathological processes, whereas its role in gestational diabetes mellitus (GDM) is unclear. This study aimed to explore the role of HCP5 in GDM. This study enrolled a total of 220 pregnant women (gestational age = 1 month). A follow-up study was performed until delivery. The occurrence of GDM was checked every month during follow-up. Plasma samples were collected from all participants and expression of HCP5 was determined with RT-qPCR. The 220 patients were divided into high and low GDM groups, and GDM-free curves were plotted for both groups and compared. The ROC curve was plotted to explore the predictive value of plasma HCP5 on the day of admission for GDM. INS-1 cells were transfected with HCP5 expression vector or siRNA, and cell viability under high glucose was determined by the MTT assay. An ELISA was applied to determine insulin levels in the cell culture medium. During follow-up, the level of HCP5 was increased during pregnancy and the high HCP5 level group showed a significantly higher incidence of GDM. Plasma levels of HCP5 on the day of admission effectively separated GDM patients from healthy controls. HCP5 negatively regulated cell viability and insulin secretion under high glucose treatment. HCP5 may act as a predictor for GDM, and it negatively regulated INS-1 cell viability and insulin secretion under high glucose conditions.

Role of Serum Hba1c Level for Prediction of Gestational Diabetes Mellitus in First Trimester

Role of Serum Hba1c Level for Prediction of Gestational Diabetes Mellitus in First Trimester

Identifying gestational diabetes mellitus and assessing risk factors in affected women: a comprehensive study.

Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication associated with adverse health outcomes for both mothers and offspring. This study aimed to identify risk factors for GDM, a condition with a rapidly increasing global prevalence. We conducted a study involving 474 pregnant women who attended the obstetrics outpatient clinic of Kafkas University Faculty of Medicine Hospital between January 2022 and June 2023. Risk factors for GDM were assessed based on criteria recommended by the American Diabetes Association and the Committee on Practice of the American College of Obstetricians and Gynecologists. Statistical analyses, including descriptive statistics, Chi-square tests, Mann-Whitney U tests, and multivariate logistic regression. Individuals with GDM (mean age: 31.26±6.09 years) were significantly older than those without GDM (mean age: 28.36±4.89 years; p<0.001). Obesity prevalence was higher in the GDM group (32.5%) compared to the non-GDM group (14.3%; p<0.001). Individuals with GDM had higher rates of pre-diabetes (3.3% vs. 0.3%; p=0.007), a history of gestational diabetes (25.2% vs. 5.7%; p<0.001), high blood sugar in previous pregnancies (13.8% vs. 1.4%; p<0.001), and diabetes mellitus in 1st-degree relatives (40.7% vs. 20.3%; p<0.001). GDM was associated with increased pregnancies (p<0.001), preterm births (p<0.001), macrosomic babies (p=0.026), congenital anomalies (p=0.011), high cholesterol (p=0.036), and polyhydramnios (p=0.001) in previous pregnancies, as well as polyhydramnios in the index pregnancy (p=0.008). Regular exercise in previous pregnancies differed significantly based on GDM presence (p=0.037). Recognizing modifiable risk factors is crucial for preventing GDM and reducing associated health risks. Healthcare providers should be vigilant, especially among those with a family history of GDM, previous GDM, advanced maternal age, and other risk factors. Early lifestyle interventions show promise. Further research is needed for accurate GDM prediction.

Prediction of Gestational Diabetes Mellitus in the First Trimester of Pregnancy Based on Maternal Variables and Pregnancy Biomarkers.

Gestational diabetes mellitus (GDM) is a significant health concern with adverse outcomes for both pregnant women and their offspring. Recognizing the need for early intervention, this study aimed to develop an early prediction model for GDM risk assessment during the first trimester. Utilizing a prospective cohort of 4917 pregnant women from the Third Department of Obstetrics and Gynecology, Aristotle University of Thessaloniki, Greece, the study sought to combine maternal characteristics, obstetric and medical history, and early pregnancy-specific biomarker concentrations into a predictive tool. The primary objective was to create a series of predictive models that could accurately identify women at high risk for developing GDM, thereby facilitating early and targeted interventions. To this end, maternal age, body mass index (BMI), obstetric and medical history, and biomarker concentrations were analyzed and incorporated into five distinct prediction models. The study's findings revealed that the models varied in effectiveness, with the most comprehensive model combining maternal characteristics, obstetric and medical history, and biomarkers showing the highest potential for early GDM prediction. The current research provides a foundation for future studies to refine and expand upon the predictive models, aiming for even earlier and more accurate detection methods.

Comparative Evaluation of Adipokine Metrics for the Diagnosis of Gestational Diabetes Mellitus.

Gestational diabetes mellitus (GDM) is one of the most common medical disorders in pregnancy. Adipokines, predominantly secreted by adipose tissue, are involved in numerous metabolic processes. The exact role of adipokines in the pathogenesis of GDM is still not well known, and numerous adipokines have been analysed throughout pregnancy and proposed as biomarkers of GDM. This study aimed to evaluate serum adiponectin, chemerin, lipocalin and apelin levels in GDM and non-GDM women, to assess them as clinically useful biomarkers of the occurrence of GDM and to demonstrate the correlation between the levels of the above adipokines in the blood serum and the increased risk of the development of GDM. The role of these adipokines in the pathogenesis of GDM was also analysed. The statistically significant differences between the levels of adiponectin (7234.6 vs. 9837.5 ng/mL, p < 0.0001), chemerin (264.0 vs. 206.7 ng/mL, p < 0.0001) and lipocalin (39.5 vs. 19.4 ng/mL, p < 0.0001) were observed between pregnant women with GDM and healthy ones. The diagnostic usefulness of the tested adipokines in detecting GDM was also assessed. The research results confirm the hypothesis on the significance of adiponectin, chemerin, lipocalin and apelin in the pathophysiological mechanisms of GDM. We speculate that these adipokines could potentially be established as novel biomarkers for the prediction and early diagnosis of GDM.