Gestational Diabetes Mellitus Model Research Articles

Fentanyl Promoted the Growth of Placenta Trophoblast Cells through Regulating the METTL14 Mediated CCL5 Levels.

Gestational diabetes mellitus (GDM) is an important cause of the increase in incidence rate and mortality of pregnant women and perinatal infants. This study aimed to analyze the role of fentanyl, a μ-opioid agonist, in the GDM progression. The high glucose (HG) treatment HTR8/SVneo cells was used as a GDM model in vitro. The cell viability was assessed with cell counting kit-8 assay. The apoptosis rate was analyzed with flow cytometry and the transwell assay was conducted to test the cell migration and invasion. RT-quantitative PCR (qPCR) assay was performed to determine the relative expressions of related genes. The N6-Methyladenosine (m6A) levels were analyzed with MeRIP analysis. The tumor necrosis factor-α (TNF-α), interleukin 1β (IL-1β), and IL-10 levels of the cells were analyzed with commercial kits. The results showed that fentanyl increased the cell viability, migration and invasion, and IL-10 levels, and declined the apoptosis rate, TNF-α and IL-1β levels of the HG stimulated HTR8/SVneo cells. The chemokine ligand 5 (CCL5) was over expressed in GDM tissues and HG stimulated HTR8/SVneo cells, which was depleted after fentanyl treatment. Over expressed CCL5 neutralized the fentanyl roles in the HG stimulated HTR8/SVneo cells. The methyltransferase-like protein 14 (METTL14) levels was decreased in HG stimulated HTR8/SVneo cells, which was up-regulated after fentanyl treatment. Additionally, METTL14 silenced prominently decreased the m6A and mRNA levels, along with the mRNA stability of CCL5. In conclusion, fentanyl promoted the growth and inhibited the apoptosis of the HG stimulated HTR8/SVneo cells through regulating the METTL14 mediated CCL5 levels.

N1-methylnicotinamide impairs gestational glucose tolerance in mice.

N1-methylnicotinamide (MNAM), a product of methylation of nicotinamide through nicotinamide N-methyltransferase, displays antidiabetic effects in male rodents. This study aimed to evaluate the ameliorative potential of MNAM on glucose metabolism in a gestational diabetes mellitus (GDM) model. C57BL/6N mice were fed with a high-fat diet (HFD) for 6 weeks before pregnancy and throughout gestation to establish the GDM model. Pregnant mice were treated with 0.3% or 1% MNAM during gestation. MNAM supplementation in CHOW diet and HFD both impaired glucose tolerance at gestational day 14.5 without changes in insulin tolerance. However, MNAM supplementation reduced hepatic lipid accumulation as well as mass and inflammation in visceral adipose tissue. MNAM treatment decreased GLUT4 mRNA and protein expression in skeletal muscle, where NAD+ salvage synthesis and antioxidant defenses were dampened. The NAD+/sirtuin system was enhanced in liver, which subsequently boosted hepatic gluconeogenesis. GLUT1 protein was diminished in placenta by MNAM. In addition, weight of placenta, fetus weight, and litter size were not affected by MNAM treatment. The decreased GLUT4 in skeletal muscle, boosted hepatic gluconeogenesis and dampened GLUT1 in placenta jointly contribute to the impairment of glucose tolerance tests by MNAM. Our data provide evidence for the careful usage of MNAM in treatment of GDM.

An in vitro Model to Study Placental Functions in Gestational Diabetes Mellitus

Background: Gestational diabetes mellitus (GDM) is a serious pregnancy complication that affects around 14 % of pregnancies worldwide. It not only causes short-term complications in the mother and child but also significantly increases the risks of several chronic diseases, including cardiovascular diseases and diabetes. An excess production of the soluble vascular endothelial growth factor (VEGF) receptor 1 in the placenta, which is a natural inhibitor of VEGF, has been linked to various pregnancy complications. However, placentas affected by GDM often exhibit increased vascularization, suggesting that the role of VEGF in GDM differs from its impact on typical pregnancy complications. Unfortunately, limited studies on placental functions in GDM have been conducted due to a lack of reliable culture models. Methods: In this study, we developed an in vitro model of GDM using the human choriocarcinoma trophoblast cell line BeWo and examined the effects of VEGF on glucose uptake. The BeWo cells were treated with a high glucose-containing (25 mM) syncytialization differentiation medium to produce insulin-resistant cells. Insulin resistance was assessed by a glucose uptake assay using the fluorescent glucose analog 2-NBDG and flow cytometry analysis. Results: The cells treated with high glucose exhibited a significant decrease in glucose uptake, suggesting the successful development of glucose resistance in these cells. However, VEGF treatment did not show a significant impact on glucose uptake in high glucose-treated cells. Conclusions: It is important to note that glucose homeostasis is a complex process involving multiple cell types, and further studies are necessary to fully understand the functions of VEGF on GDM placentas. Nevertheless, the in vitro model we have developed and validated will be a valuable tool for studying placental pathophysiology in GDM and evaluating the effectiveness of potential therapeutic agents. HIGHLIGHTS BeWo cells are the choriocarcinoma cell line which were cultured in the in vitro condition BeWo cells were differentiated using forskolin and 8-Br Camp Insulin resistant model of BeWo cells were prepared by high glucose (25 mM) treatment The effect of VEGF on glucose homeostasis in GDM model of BeWo cells was observed GRAPHICAL ABSTRACT

Establishment and evaluation of a risk prediction model for gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is a condition characterized by high blood sugar levels during pregnancy. The prevalence of GDM is on the rise globally, and this trend is particularly evident in China, which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses. Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses. Therefore, this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin, blood glucose, and body mass index (BMI) on the occurrence of GDM. To develop a risk prediction model to analyze factors leading to GDM, and evaluate its efficiency for early prevention. The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed. According to whether GDM occurred, they were divided into two groups to analyze the related factors affecting GDM. Then, according to the weight of the relevant risk factors, the training set and the verification set were divided at a ratio of 7:3. Subsequently, a risk prediction model was established using logistic regression and random forest models, and the model was evaluated and verified. Pre-pregnancy BMI, previous history of GDM or macrosomia, hypertension, hemoglobin (Hb) level, triglyceride level, family history of diabetes, serum ferritin, and fasting blood glucose levels during early pregnancy were de-termined. These factors were found to have a significant impact on the development of GDM (P < 0.05). According to the nomogram model's prediction of GDM in pregnancy, the area under the curve (AUC) was determined to be 0.883 [95% confidence interval (CI): 0.846-0.921], and the sensitivity and specificity were 74.1% and 87.6%, respectively. The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin, fasting blood glucose in early pregnancy, pre-pregnancy BMI, Hb level and triglyceride level. The random forest model achieved an AUC of 0.950 (95%CI: 0.927-0.973), the sensitivity was 84.8%, and the specificity was 91.4%. The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model (P < 0.05). The random forest model is superior to the nomogram model in predicting the risk of GDM. This method is helpful for early diagnosis and appropriate intervention of GDM.

Exploration of Serum lipid levels during twin pregnancy

Objective: This study aims to characterize changes in serum lipid levels throughout twin pregnancies and explore the relationship between lipid levels and gestational diabetes mellitus (GDM) and hypertensive disorders complicating pregnancy (HDCP). Methods: We retrospectively studied 297 twin pregnancies of women who received regular prenatal care and delivered at the Beijing Obstetrics and Gynecology Hospital over a period of two years. Demographic and medical data of the participants were collected by questionnaires and medical records review. Serum lipid levels were measured in the first trimester (6–13 weeks), second trimester (24–28 weeks), and third trimester (34–37 weeks). A multivariate regression model was constructed to examine the association between lipid levels and pregnancy complications. A decision tree was used to explore the relationship between early serum lipid glucose levels and GDM and HDCP in twin pregnancies. Results: Triglyceride (TG), total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels increased significantly from the first trimester to the third trimester, with the exception of high-density lipoprotein cholesterol (HDL-C), which decreased in the third trimester in twin pregnancies (p < 0.001). The levels of TC in the GDM and HDCP group were significantly elevated compared to those in the normal group in early pregnancies (p < 0.05, p < 0.05). In the second trimester, TG in the HDCP group was substantially higher than that in the normal group (p = 0.01). In the third trimester, LDL-C and HDL-C levels in the GDM group are significantly lower than that in the normal group (p < 0.05, p < 0.05). After adjusting for confounders, body mass index (BMI) is independently associated with GDM (odds ratio [OR] = 1.129, 95% confidence interval [CI]: 1.007–1.266) and HDCP(odds ratio [OR] = 1.170, 95% confidence interval [CI]: 1.031–1.329). The variation amplitude of HDL-C in the third trimester is related to the occurrence of GDM and HDCP(GDM:OR = 0.271, 95%CI: 0.095–0.778; HDCP: OR =0.249, 95% CI: 0.075–0.823). TG and TC levels in DCDA twins were significantly higher than that in MCDA twins in the first trimester(TG: p < 0.05, TC: p < 0.05). In the decision tree model for GDM, fasting blood glucose in the first trimester (FBG), TC, and pre-pregnancy BMI were identified as important nodes, while in the HDCP model, pre-pregnancy BMI, TC, and TG were key nodes. Conclusion: Serum lipid levels in twin pregnancies increase gradually during pregnancy. BMI is independently associated with the occurrence of GDM and HDCP. HDL-C may serve as a protective factor for GDM and HDCP. The predictive effect of early blood lipid on GDM and HDCP in twin pregnancy needs further study.

A Quinoa Protein Hydrolysate Fractionated by Electrodialysis with Ultrafiltration Membranes Improves Maternal and Fetal Outcomes in a Mouse Model of Gestational Diabetes Mellitus.

Quinoa intake exerts hypoglycemic and hypolipidemic effects in animals and humans. Although peptides from quinoa inhibit key enzymes involved in glucose homeostasis in vitro, their in vivo antidiabetic properties have not been investigated. Thisstudy evaluatedthe effect of oral administration of a quinoa protein hydrolysate (QH) produced through enzymatic hydrolysis and fractionation by electrodialysis with ultrafiltration membrane (EDUF) (FQH) on the metabolic and pregnancy outcomes of Lepdb/+ pregnant mice, a preclinical model of gestational diabetes mellitus. The 4-week pregestational consumption of 2.5mgmL-1 of QH in water prevented glucose intolerance and improves hepatic insulin signaling in dams, also reducing fetal weights. Sequencing and bioinformatic analyses of the defatted FQH (FQHD) identified 11 peptides 6-10 amino acids long that aligned with the quinoa proteome and exhibited putative anti-dipeptidyl peptidase-4 (DPP-IV) activity, confirmed in vitro in QH, FQH, and FDQH fractions. Peptides homologous to mouse and human proteins enriched for biological processes related to glucose metabolism are also identified. Processing of quinoa protein may be used to develop a safe and effective nutritional intervention to control glucose intolerance during pregnancy. Further studies are required to confirm if this nutritional intervention is applicable to pregnant women.

Pengaruh Pemberian Sari Ketela Ungu (Ipomea Batatas L.) Terhadap Kadar Insulin Tikus Wistar Bunting Model DMG

Background : Gestational Diabetes Mellitus is a complication of pregnancy in mothers without a previous diagnosis of diabetes, then chronic hyperglycemia is detected during pregnancy. Generally, this hyperglycemia is caused by impaired glucose tolerance due to pancreatic cell dysfunction, which begins with chronic insulin resistance conditions. The prevalence of GDM in Southeast Asia is 10%, while the overall diagnosis of diabetes in women in Indonesia is 1.8%. Nowadays, antioxidant therapy is being developed to treat DMG, one of which is the use of purple cassava plants.Objective : to determine the effect of purple cassava juice (Ipomea Batatas L.) on increasing insulin levels of wistar rats model of gestational diabetes mellitus. Methode : This study used an experimental method which was designed with the Post Test Only Group Design method. The total number of samples used in this study was 30 rats which were divided into 5 groups. Furthermore, the treatment group was given STZ injection of 45 mg/KgBB and then given purple cassava juice therapy for 14 days. Next, blood samples were tested for insulin levels using the ELISA Rat Kit. Result : There are no differences of insulin level between the treatment group and the control group because they had constant result. Conclusion : In this study, it was not found that there was an increase in insulin levels after treatment of purple yam juice for 14 days. So the research hypothesis was rejected. Keyword : Diabetes Mellitus Gestasional, Insulin Levels, Wistar Rats, Purple Yam Juice.

Two-hits, two-goals: An improved model for gestational diabetes mellitus and a novel link to the development of non-alcoholic fatty liver disease.

Two-hits, two-goals: An improved model for gestational diabetes mellitus and a novel link to the development of non-alcoholic fatty liver disease.

Calcium/calmodulin dependent protein kinase IV in trophoblast cells under insulin resistance: functional and metabolomic analyses

BackgroundInsulin resistance (IR) is an important determinant of glucose metabolic disturbance and placental dysplasia in gestational diabetes mellitus (GDM). Calcium/calmodulin dependent protein kinase IV (CAMK4) improves insulin IR induced by a high-fat diet (HFD). The current study sought to elucidate the role and potential mechanism of CAMK4 in GDM.MethodsA GDM model was established in female C57BL/6J mice via HFD feeding for one week before mating and throughout gestation. The IR was elicited by 10–6 M insulin treatment for 48 h in HTR-8/SVneo cells and mouse primary trophoblast cells. The function of CAMK4 was investigated by transfection of overexpression plasmid in HTR-8/SVneo cells and infection of lentivirus loaded with CAMK4 encoding sequence in primary trophoblast cells. Real-time PCR, western blot, cell counting kit-8, transwell, wound healing, dual-luciferase reporter assay, and liquid chromatography/mass spectrometry-based untargeted metabolomics were performed to confirm the effects of CAMK4 on trophoblast cells.ResultsDecreased CAMK4 expression was found in the placenta of GDM mice. CAMK4 overexpression ameliorated IR-induced viability impairment, migratory and invasive capacity inhibition, autophagy blocking, insulin signaling inactivation and glucose uptake disorder in trophoblast cells. CAMK4 also transcriptionally activated orphan nuclear receptor NUR77, and the effects of CAMK4 were abrogated by silencing of NUR77. Metabolomics analysis revealed that CAMK4 overexpression caused alterations of amino acid, lipid and carbohydrate metabolism, which were important in GDM.ConclusionOur results indicated that CAMK4/NUR77 axis may provide novel potential targets in GDM treatment.

DNA methylation analysis reveals the effect of arsenic on gestational diabetes mellitus

BackgroundArsenic (As) exposure is one of the risk factors for gestational diabetes mellitus (GDM). This study aimed to explore the effect of As-exposure on DNA methylation in GDM and to establish a risk assessment model of GDM in As exposed pregnant women. MethodWe collected elbow vein blood of pregnant women before delivery to measure As concentration and DNA methylation data. Then compared the DNA methylation data and established a nomogram. ResultWe identified a total of 10 key differentially methylated CpGs (DMCs) and found 6 corresponding genes. Functions were enriched in Hippo signaling pathway, cell tight junction, prophetic acid metabolism, ketone body metabolic process, and antigen processing and presentation. A nomogram was established that can predict GDM risks (c-index = 0.595, s:p = 0.973). ConclusionWe found 6 genes associated with GDM with high As exposure. The prediction of the nomograms has been proven to be effective.

1175-P: The Alteration of Serum Level and Expression of c-MET in Gestational Diabetes Mellitus (GDM)

Objective: The aim of this study was to explore changes in serum levels and tissue expression of HGF and c-MET, and to determine how HGF/c-MET signaling contributes to the pathophysiology of GDM. Methods: A total of 44 pregnant women with normal glucose tolerance (NGT) and 32 pregnant women with GDM were studied. Blood samples were taken just before delivery, and placenta and visceral adipose tissue were taken immediately after cesarean delivery. Serum levels of HGF, soluble c-MET and insulin were measured by enzyme-linked immunosorbent assay (ELISA). The mRNA expression of HGF and c-MET was measured in the placenta and visceral adipose tissue. In addition, we investigated pregnancy of high fat diet (HFD) mice as an animal model of GDM. The glucose tolerance test (GTT) was performed at GD 10, and measured the serum levels of Hgf and soluble c-Met at GD 16. After scarifying mice, we measured the weight and expression of mRNA of Hgf and c-Met in WAT, placenta, liver and muscle. Results: Serum soluble c-MET levels were significantly higher in the GDM group (p &amp;lt;0.001). In multiple logistic regression analysis, soluble serum c-MET level was strongly associated with GDM (OR 9.063, p=0.027). There were more patients with increased mRNA expression of HGF and c-MET in placenta. Interestingly, the protein expression of HGF and c-MET in maternal visceral fat tissue was significantly higher in the GDM group. In mouse study, maternal serum levels of soluble c-Met in pregnant HFD (PH) mice were higher than in non-pregnant HFD (NPH) mice and pregnant chow diet (PC) mice. Relative mRNA expression of Hgf was significantly increased in eWAT of PH than PC mice. Relative c-Met mRNA expression was significantly increased in sWAT of PH than PC mice. Conclusion: We observed the differences in serum levels and expression of HGF and c-MET between GDM and control pregnant women in human and mice model. Our results suggest that HGF/c-MET signaling may be altered in GDM, which associated with insulin resistance and pathophysiology in GDM. Disclosure H.Kim: None. J.Lee: None. Y.Jung: None. M.Lee: None. J.Kim: None. K.Joung: None. B.Ku: Speaker's Bureau; AstraZeneca, Boehringer-Ingelheim, Eli Lilly and Company, Sanofi, Novo Nordisk, LG Chem. Funding National Research Foundation of Korea (2022R1F1A1074608)

An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers

BackgroundGestational diabetes mellitus (GDM) represents the main metabolic alteration during pregnancy. The available methods for diagnosing GDM identify women when the disease is established, and pancreatic beta-cell insufficiency has occurred.The present study aimed to generate an early prediction model (under 18 weeks of gestation) to identify those women who will later be diagnosed with GDM.MethodsA cohort of 75 pregnant women was followed during gestation, of which 62 underwent normal term pregnancy and 13 were diagnosed with GDM. Targeted metabolomics was used to select serum biomarkers with predictive power to identify women who will later be diagnosed with GDM.ResultsCandidate metabolites were selected to generate an early identification model employing a criterion used when performing Random Forest decision tree analysis. A model composed of two short-chain acylcarnitines was generated: isovalerylcarnitine (C5) and tiglylcarnitine (C5:1). An analysis by ROC curves was performed to determine the classification performance of the acylcarnitines identified in the study, obtaining an area under the curve (AUC) of 0.934 (0.873–0.995, 95% CI). The model correctly classified all cases with GDM, while it misclassified ten controls as in the GDM group. An analysis was also carried out to establish the concentrations of the acylcarnitines for the identification of the GDM group, obtaining concentrations of C5 in a range of 0.015–0.25 μmol/L and of C5:1 with a range of 0.015–0.19 μmol/L.ConclusionEarly pregnancy maternal metabolites can be used to screen and identify pregnant women who will later develop GDM. Regardless of their gestational body mass index, lipid metabolism is impaired even in the early stages of pregnancy in women who develop GDM.

Regulation of uterus and placenta remodeling under high estradiol levels in gestational diabetes mellitus models†.

The present study aimed to investigate the regulation of placentas and uterus remodeling and involvement of E2 in gestational diabetes mellitus (GDM). To achieve this, we established in vitro and in vivo models for GDM placentas by culturing BeWo cells under hyperglycemic concentration, and treating pregnant rats with streptozotocin (STZ). We evaluated the expression of angiogenesis-related proteins. The expression of the anti-angiogenic factor, excess placental soluble fms-like tyrosine kinase 1 (sFLT1) was increased in our in vitro GDM model compared to the control. Moreover, the expressions of placental sFLT1 and the von willebrand factor (VWF) were also significantly elevated in the placenta of STZ-treated rats. These data indicate the disruption of angiogenesis in the GDM placentas. The expression levels of connexin 43 (Cx43), a component of the gap junction and collagen type I alpha 2 chain (Col1a2), a component of the extracellular matrix, were decreased in the GDM uterus. These results suggest that uterus decidualization and placental angiogenesis are inhibited in GDM rats. Our results also showed upregulation of the expression of genes regulating E2 synthesis as well as estrogen receptors (ER) in vivo models. Accordingly, the concentration of E2 measured in the culture medium under hyperglycemic conditions, as well as in the serum and placenta of the STZ-treated rats, was significantly elevated compared to the control groups. These results suggest that the dysregulated remodeling of the placenta and uterus may result in the elevation of E2 and its signaling pathway in the GDM animal model to maintain pregnancy.

Gestational diabetes mellitus care re‐imagined – A cost‐minimisation analysis: Cost savings from a tertiary hospital, using a novel, digital‐based gestational diabetes management model

Diagnosis of gestational diabetes mellitus (GDM) in a pregnancy has a significant impact on health service resources and represents a substantial financial and time impost on women. To describe a cost-minimisation analysis conducted following the demonstration of clinically equivalent care of women using a novel, digital model for GDM management, compared with conventional care. A pre-implementation model of care was compared with the post-implementation model of care which included systematic development and delivery of education videos, use of the Commonwealth Scientific and Industrial Research Organisation 'M♡THer' smart phone app/portal and a dramatically reduced schedule of visits. The Mater Mothers' Hospital Brisbane cares for approximately 1200 women with GDM per annum, on which the cost estimates were based. Service costs were estimated using the resource method, where resource volumes and costs were gathered from experts within the health service. Patient costs were estimated using results from a short survey completed by a cohort of the study population. Health service costs showed a modest saving of AU$17 441.78 (US$12 158.92) in the intervention group over a 12-month period. Cost savings for the woman were estimated at $566.56 (US$394.96) per patient after accounting for lost wages, childcare expenses, and travel expenses avoided. This reduction led to an overall saving of $679 872 (US$473 948.82) for the cohort of 1200 women, primarily due to the reduction in face-to-face visits. Re-imagining GDM patient care by introducing a novel, digital-based GDM model of care has substantial positive cost implications for patients.

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.

Mouse Models of Gestational Diabetes Mellitus and Its Subtypes: Recent Insights and Pitfalls.

Gestational diabetes mellitus (GDM) is currently the most common complication of pregnancy and is defined as a glucose intolerance disorder with recognition during pregnancy. GDM is considered a uniform group of patients in conventional guidelines. In recent years, evidence of the disease's heterogeneity has led to a growing understanding of the value of dividing patients into different subpopulations. Furthermore, in view of the increasing incidence of hyperglycemia outside pregnancy, it is likely that many cases diagnosed as GDM are in fact patients with undiagnosed pre-pregnancy impaired glucose tolerance (IGT). Experimental models contribute significantly to the understanding of the pathogenesis of GDM and numerous animal models have been described in the literature. The aim of this review is to provide an overview of the existing mouse models of GDM, in particular those that have been obtained by genetic manipulation. However, these commonly used models have certain limitations in the study of the pathogenesis of GDM and cannot fully describe the heterogeneous spectrum of this polygenic disease. The polygenic New Zealand obese (NZO) mouse is introduced as a recently emerged model of a subpopulation of GDM. Although this strain lacks conventional GDM, it exhibits prediabetes and an IGT both preconceptionally and during gestation. In addition, it should be emphasized that the choice of an appropriate control strain is of great importance in metabolic studies. The commonly used control strain C57BL/6N, which exhibits IGT during gestation, is discussed in this review as a potential model of GDM.

Evaluation of fetoplacental oxygenation in a rat model of gestational diabetes mellitus using BOLD-MRI at 7.0-T

Objective: To compare the differences in blood oxygen level-dependent (BOLD) parameters following maternal hyperoxia between normal pregnancy and pregnancy in the rat model of gestational diabetes mellitus (GDM). Methods: GDM was induced by high-fat and sucrose diet (HFS) combined with an intraperitoneal injection of streptozotocin (STZ). On embryonic day 19 (E19), the two groups of pregnant rats were imaged using a 7.0-T animal MRI scanner. TurboRARE was initially used to localize the fetoplacental units (FPUs). Next, multiple gradient echo BOLD was performed during the air and oxygen inhalation periods. T2* map was then generated, and the baseline T2* and absolute changes in T2* value (ΔT2*, difference between T2*oxy and T2*air) were calculated. Following the MRI scan, the placentas and fetuses were aseptically stripped, weighed, and immunostained. Results: Nine rats were used in this study. After maternal oxygen inhalation, T2* increased significantly in all subjects in both groups. The ΔT2* for the placenta (5.97 vs. 7.81 msec; P = 0.007) and fetal brain (2.23 vs. 3.97 msec; P = 0.005) differed significantly between the GDM and control groups. Histochemical detection of placental glycogen content and inflammatory cytokines (IL-6 and TNF-α) showed significantly higher levels in the GDM than in the normal placenta. Conclusions: BOLD-MRI revealed abnormalities in the fetoplacental response to maternal hyperoxygenation in rats with GDM. We believe that this approach can potentially be used to evaluate placental dysfunction and assess the state of the fetus during pregnancy with GDM.

A Prediction Model of Gestational Diabetes Mellitus Based on OGTT in Early Pregnancy: A Prospective Cohort Study.

Gestational diabetes mellitus (GDM) is a common obstetric complication. Although early intervention could prevent the development of GDM, there was no consensus on early identification for women at high risk of GDM. To develop a reliable prediction model of GDM in early pregnancy. In this prospective cohort study, between May 30, 2021, and August 13, 2022, a total of 721 women were included from Women's Hospital, Zhejiang University School of Medicine. Participants were asked to complete an oral glucose tolerance test (OGTT) during gestational weeks 7 through 14 for early prediction of GDM, and at weeks 24 through 28 for GDM diagnosis. Using OGTT results and baseline characteristics, logistic regression analysis was used to construct the prediction model. Receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, decision clinical analysis, and a nomogram were used for model performances assessment and visualization. Internal and external validation was performed to testify the stability of this model. According to the International Association of Diabetes and Pregnancy Study Groups criteria in early OGTT, the mean (SD) age was 30.5 ± 3.7 years in low-risk participants and 31.0 ± 3.9 years in high-risk participants. The area under ROC curve (AUC) of the existing criteria at weeks 7 through 14 varied from 0.705 to 0.724. Based on maternal age, prepregnancy body mass index, and results of early OGTT, the AUC of our prediction model was 0.8720, which was validated by both internal (AUC 0.8541) and external (AUC 0.8241) confirmation. The existing diagnostic criteria were unsatisfactory for early prediction of GDM. By combining early OGTT, we provided an effective prediction model of GDM in the first trimester.

Descriptive Analysis of Lipid Panels and Other Biomarkers in Early and Late Pregnancy in the Hoosier Moms Cohort

Background/Objective: Early identification of and intervention for pregnant persons with a high risk of developing gestational diabetes mellitus (GDM) may lead to better health outcomes for both mother and infant. We aimed to assess both biomarkers and clinical predictors in early and late pregnancy to aid in the future development of an early pregnancy predictive model for GDM.&#x0D; Methods: This project is part of the Hoosier Moms Cohort, a prospective, longitudinal cohort study of pregnant women followed from early pregnancy through the postpartum period. Blood samples were taken in early and late pregnancy to discover clinical laboratory biomarkers which could be used for GDM risk prediction. Other pertinent medical history and findings were collected through patient surveys and chart abstraction. Data analysis included general descriptive statistics, Pearson’s correlation, and nonparametric tests.&#x0D; Results: 409 participants’ data were analyzed. Many biomarkers weakly correlated with maternal age and BMI in early and late pregnancy. Early pregnancy HbA1c was moderately correlated with BMI (0.32, p&lt;0.001), and fructosamine was moderately negatively correlated with BMI (-0.43, p&lt;0.001). Total cholesterol (TC), fructosamine, HDL, LDL, and triglycerides (TG) were weakly correlated with maternal age in early pregnancy. Blood glucose (BG) and TG were weakly correlated with BMI, and HDL was weakly negatively correlated with BMI in early pregnancy. Similar correlations were found with the biomarkers later in pregnancy with maternal age and BMI. White women had significantly lower HbA1c and higher TC and HDL levels when compared to Black/African American and “other race/ethnicity” women. Black/African American women had significantly lower fructosamine levels. LDL and TG were significantly lower and higher in “other race/ethnicity” women, respectively.&#x0D; Conclusion: BMI and race/ethnicity have significant relationships with biomarkers in early pregnancy that must be adjusted for in the development of a GDM predictive model.

Long Non-Coding RNA TUG1 Attenuates Insulin Resistance in Mice with Gestational Diabetes Mellitus via Regulation of the MicroRNA-328-3p/SREBP-2/ERK Axis.

Long non-coding RNAs (lncRNAs) have been illustrated to contribute to the development of gestational diabetes mellitus (GDM). In the present study, we aimed to elucidate how lncRNA taurine upregulated gene 1 (TUG1) influences insulin resistance (IR) in a high-fat diet (HFD)-induced mouse model of GDM. We initially developed a mouse model of HFD-induced GDM, from which islet tissues were collected for RNA and protein extraction. Interactions among lncRNA TUG1/microRNA (miR)-328-3p/sterol regulatory element binding protein 2 (SREBP-2) were assessed by dual-luciferase reporter assay. Fasting blood glucose (FBG), fasting insulin (FINS), homeostasis model assessment of insulin resistance (HOMA-IR), HOMA pancreatic β-cell function (HOMA-β), insulin sensitivity index for oral glucose tolerance tests (ISOGTT) and insulinogenic index (IGI) levels in mouse serum were measured through conducting gain- and loss-of-function experiments. Abundant expression of miR-328 and deficient expression of lncRNA TUG1 and SREBP-2 were characterized in the islet tissues of mice with HFD-induced GDM. LncRNA TUG1 competitively bound to miR-328-3p, which specifically targeted SREBP-2. Either depletion of miR-328-3p or restoration of lncRNA TUG1 and SREBP-2 reduced the FBG, FINS, HOMA-β, and HOMA-IR levels while increasing ISOGTT and IGI levels, promoting the expression of the extracellular signal-regulated kinase (ERK) signaling pathway-related genes, and inhibiting apoptosis of islet cells in GDM mice. Upregulation miR-328-3p reversed the alleviative effects of SREBP-2 and lncRNA TUG1 on IR. Our study provides evidence that the lncRNA TUG1 may prevent IR following GDM through competitively binding to miR-328-3p and promoting the SREBP-2-mediated ERK signaling pathway inactivation.