Gestational Diabetes Mellitus Model Research Articles

Phenotypes of streptozotocin-induced gestational diabetes mellitus in mice.

Gestational diabetes mellitus (GDM) in human patients disrupts glucose metabolism post-pregnancy, affecting fetal development. Although obesity and genetic factors increase GDM risk, a lack of suitable models impedes a comprehensive understanding of its pathology. To address this, we administered streptozotocin (STZ, 75 mg/kg) to C57BL/6N mice for two days before pregnancy, establishing a convenient GDM model. Pregnant mice exposed to STZ (STZ-pregnant) were compared with STZ-injected virgin mice (STZ-virgin), citrate buffer-injected virgin mice (CB-virgin), and pregnant mice injected with citrate buffer (CB-pregnant). STZ-pregnant non-obese mice exhibited elevated blood glucose levels on gestational day 15.5 and impaired glucose tolerance. They also showed fewer normal fetuses compared to CB-pregnant mice. Additionally, STZ-pregnant mice had the highest plasma C-peptide levels, with decreased pancreatic islets or increased alpha cells compared to CB-pregnant mice. Kidneys isolated from STZ-pregnant mice did not display histological alterations or changes in gene expression for the principal glucose transporters (GLUT2 and SGLT2) and renal injury-associated markers. Notably, STZ-pregnant mice displayed decreased gene expression of insulin-receiving molecules (ISNR and IGFR1), indicating heightened insulin resistance. Liver histology in STZ-pregnant mice remained unchanged except for a pregnancy-related increase in lipid droplets within hepatocytes. Furthermore, the duodenum of STZ-pregnant mice exhibited increased gene expression of ligand-degradable IGFR2 and decreased expression of GLUT5 and GLUT12 (fructose and glucose transporters, respectively) compared to STZ-virgin mice. Thus, STZ-pregnant mice displayed GDM-like symptoms, including fetal abnormalities, while organs adapted to impaired glucose metabolism by altering glucose transport and insulin reception without histopathological changes. STZ-pregnant mice offer a novel model for studying mild onset non-obese GDM and species-specific differences in GDM features between humans and animals.

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).

A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Caucasian and Latin American Pregnant Women.

The pathophysiology of gestational diabetes mellitus (GDM) comprises clinical and genetic factors. In fact, GDM is associated with several single nucleotide polymorphisms (SNPs). This study aimed to build a prediction model of GDM combining clinical and genetic risk factors. A total of 1588 pregnant women from the San Carlos Cohort participated in the present study, including 1069 (67.3%) Caucasian (CAU) and 519 (32.7%) Latin American (LAT) individuals, and 255 (16.1%) had GDM. The incidence of GDM was similar in both groups (16.1% CAU and 16.0% LAT). Genotyping was performed via IPLEX Mass ARRAY PCR, selecting 110 SNPs based on literature references. SNPs showing the strongest likelihood of developing GDM were rs10830963, rs7651090, and rs1371614 in CAU and rs1387153 and rs9368222 in LAT. Clinical variables, including age, pre-pregnancy body mass index, and fasting plasma glucose (FPG) at 12 gestational weeks, predicted the risk of GDM (AUC 0.648, 95% CI 0.601-0.695 in CAU; AUC 0.688, 95% CI 0.628-9.748 in LAT), and adding SNPs modestly improved prediction (AUC 0.722, 95%CI 0.680-0.764 in CAU; AUC 0.769, 95% CI 0.711-0.826 in LAT). In conclusion, adding genetic variants enhanced the prediction model of GDM risk in CAU and LAT pregnant women.

Sh- Ambra1 inhibits IRS-1/PI3K/Akt signalling pathway to reduce autophagy in gestational diabetes.

Gestational diabetes mellitus (GDM) is the most common metabolic disease in pregnancy. However, studies of activating molecule of Beclin1-regulated autophagy (Ambra1) affecting the insulin substrate receptor 1/phosphatidylinositol 3 kinase/protein kinase B (IRS-1/PI3K/Akt) signalling pathway in GDM have not been reported. The aim of the study was to detect the difference of Ambra1 expression in the placenta of normal pregnant women and GDM patients. An in vitro model of gestational diabetes mellitus was established by inducing HTR8/Svneo cells from human chorionic trophoblast layer with high glucose. The changes of cell morphology were observed by inverted microscope, and the expression levels of Ambra1 gene and protein in model cells were detected. After this, Ambra1 gene was silenced by shRNA transfection, and PI3K inhibitor was added to detect changes in Ambra1, autophagy, and insulin (INS) signalling pathways. The protein expression levels of Ambra1, Bcl-2 interacting protein (Beclin-1), and microtubule-associated proteins 1A/1B light chain 3B (LC3-II) in the placentas of GDM pregnant women were higher than those of normal pregnant women. High glucose induces morphological changes in HTR8/Svneo cells and increases Ambra1 transcription and translation levels. sh-Ambra1 increased survival of HTR8/SvNEO-HG cells and inhibited Ambra1, Beclin1, and LC3-II transcription and translation levels. Also, sh-Ambra1 increased IRS-1/PI3K/Akt protein phosphorylation levels and inhibited the IRS-1/PI3K/Akt signalling pathway and its resulting autophagy. sh-Ambra1 increased IRS-1/PI3K/Akt protein phosphorylation levels to reduce autophagy in gestational diabetes.

ATP5me alleviates high glucose-induced myocardial cell injury

BackgroundGestational diabetes mellitus (GDM) is associated with adverse myocardial remodeling and impaired cardiac function of fetus. Nevertheless, specific molecular mechanisms underlying type 1 GDM-induced fetal myocardial injury remain unknown. Therefore, this study proposes to identify possible molecular mechanisms using RNA-seq. MethodsA rat type 1 GDM model was developed using streptozotocin (STZ) (25 and 50 mg/kg), and weight and glucose tolerance of maternal and offspring were evaluated. Changes in markers of myocardial injury and oxidative stress identified by ELISA and biochemical kits in offspring hearts. Identification of differentially expressed mRNAs (DE-mRNAs) associated with myocardial injury in type 1 GDM offspring using RNA-seq. Proliferation, apoptosis, and oxidative stress were assessed in high glucose-induced H9C2 cells after exogenously modulating ATP Synthase Membrane Subunit E (ATP5me). ResultsMaternal weight, glucose and glucose tolerance, and fetal weight and heart weight were reduced in the type 1 GDM model, especially in 50 mg/kg STZ-induced. Increased of creatine kinase-MB (CK-MB), cardiac troponin T (cTnT), hypersensitive C-reactive protein (hs-CRP), reactive oxygen species (ROS) and malondialdehyde (MDA) and decreased of superoxide dismutase (SOD) were observed in type 1 GDM offspring hearts. type 1 GDM offspring hearts exhibited disorganized cardiomyocytes with enlarged gaps, broken myocardial fibers, erythrocyte accumulation and inflammatory infiltration. RNA-seq identified 462 DE-mRNAs in type 1 GDM offspring hearts, which mainly regulate immunity, redox reactions, and cellular communication. Atp5me was under-expressed in type 1 GDM offspring hearts, and high glucose decreased Atp5me expression in H9C2 cells. Overexpressing Atp5me alleviated high glucose-induced decrease in proliferation, mitochondrial membrane potential, BCL2 and SOD, and increase in apoptosis, MDA, ROS, c-Caspase-3, and BAX in H9C2 cells. ConclusionThis study first demonstrated that ATP5me attenuated type 1 GDM-induced fetal myocardial injury. This provides a possible molecular mechanism for the treatment of type 1 GDM-induced fetal myocardial injury.

Gestational diabetes augments group B Streptococcus infection by disrupting maternal immunity and the vaginal microbiota

Group B Streptococcus (GBS) is a pervasive perinatal pathogen, yet factors driving GBS dissemination in utero are poorly defined. Gestational diabetes mellitus (GDM), a complication marked by dysregulated immunity and maternal microbial dysbiosis, increases risk for GBS perinatal disease. Using a murine GDM model of GBS colonization and perinatal transmission, we find that GDM mice display greater GBS in utero dissemination and subsequently worse neonatal outcomes. Dual-RNA sequencing reveals differential GBS adaptation to the GDM reproductive tract, including a putative glycosyltransferase (yfhO), and altered host responses. GDM immune disruptions include reduced uterine natural killer cell activation, impaired recruitment to placentae, and altered maternofetal cytokines. Lastly, we observe distinct vaginal microbial taxa associated with GDM status and GBS invasive disease status. Here, we show a model of GBS dissemination in GDM hosts that recapitulates several clinical aspects and identifies multiple host and bacterial drivers of GBS perinatal disease.

Gestational Diabetes Mellitus Remodels the Fetal Brain Fatty Acid Profile Through Placenta-Brain Lipid Axis in C57BL/6J Mice

BackgroundPolyunsaturated fatty acids (PUFAs), especially docosahexaenoic acid (DHA), are critical for proper fetal brain growth and development. Gestational diabetes mellitus (GDM) could affect maternal–fetal fatty acid metabolism. ObjectiveThis study aimed to explore the effect of GDM and high-fat (HF) diet on the DHA transport signaling pathway in the placenta-brain axis and fatty acid concentrations in the fetal brain. MethodsInsulin receptor antagonist (S961) and HF diet were used to establish an animal model of GDM. Eighty female C57BL/6J mice were randomly divided into control (CON), GDM, HF, and HF+GDM groups. The fatty acid profiles of the maternal liver and fetal brain were analyzed by gas chromatography. In addition, we analyzed the protein amounts of maternal liver fatty acid desaturase (FADS1/3), elongase (ELOVL2/5) and the regulatory factor sterol-regulatory element-binding protein (SREBP)-1c, and the DHA transport signaling pathway (Wnt3/β-catenin/MFSD2a) of the placenta and fetal brain using western blotting. ResultsGDM promoted the decrease of maternal liver ELOVL2, ELOVL5, and SREBP-1c. Accordingly, we observed a significant decrease in the amount of maternal liver arachidonic acid (AA), DHA, and total n–3 PUFA and n–6 PUFA induced by GDM. GDM also significantly decreased the amount of DHA and n–3 PUFA in the fetal brain. GDM downregulated the Wnt3/β-catenin/MFSD2a signaling pathway, which transfers n–3 PUFA in the placenta and fetal brain. The HF diet increased n–6 PUFA amounts in the maternal liver, correspondingly increasing linoleic acid, gamma-linolenic acid, AA, and total n–6 PUFA in the fetal brain, but decreased DHA amount in the fetal brain. However, HF diet only tended to decrease placental β-catenin and MFSD2a amounts (P = 0.074 and P = 0.098, respectively). ConclusionsMaternal GDM could affect the fatty acid profile of the fetal brain both by downregulating the Wnt3/β-catenin/MFSD2a pathway of the placental–fetal barrier and by affecting maternal fatty acid metabolism.

Establishment of gestational diabetes risk prediction model and clinical verification

ObjectiveThe present study aimed to evaluate the risk factors for gestational diabetes mellitus (GDM) and build and validate an early risk prediction model of GDM by comparing the differences in the indicators of the first trimester of pregnancy between pregnant women with GDM and non-gestational diabetes mellitus (NGDM). Thus, this study provided a theoretical basis for early intervention of GDM.MethodsA total of 6000 pregnant women who underwent a routine prenatal examination in Qinhuangdao Maternal and Child Health Hospital (Qinhuangdao City, Hebei Province, China) from January 2016–2022 were retrospectively selected and randomly divided into a modeling cohort (4200 cases) and validation cohort (1800 cases) at a ratio of 3:7. According to the results of oral glucose tolerance test (OGTT), they were divided into NGDM and GDM groups. The modeling cohort consisted of 2975 NGDM and 1225 GDM cases, while the validation cohort consisted of 1281 NGDM and 519 GDM cases. The differences in general conditions and laboratory indicators between different groups were compared, and logistic regression analysis was further used to establish a risk prediction model for GDM in the first trimester. The receiver operating characteristic curve (ROC) and Hosmer–Lemeshow (HL) tests were used to evaluate the prediction of the model efficacy.ResultsAge, pre-pregnancy body mass index (BMI), glycosylated hemoglobin (HbA1c), blood uric acid (UA), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) in the first trimester were independent risk factors for GDM (P < 0.05). The model equation was Y = 1/{1 + exp[− (− 18.373 + age × 0.065 + BMI × 0.030 + first-trimester HbA1c × 2.519 + UA × 0.014 + TG × 0.224-HDL-C × 0.635)]}. The area under the ROC curve (AUC) of the model cohort was 0.803 (0.788–0.817), the sensitivity was 72.0%, and the specificity was 73.5%. The AUC of the validation cohort was 0.782 (0.759–0.806), the sensitivity was 68.6%, and the specificity was 73.8%. The P values of the HL test in both the training and validation sets were > 0.05, indicating a satisfactory model fit.ConclusionAge, pre-pregnancy BMI, HbA1C in early pregnancy, blood UA, TG, and HDL-C are independent risk factors for GDM. The risk prediction model established by combining age, pre-pregnancy BMI, and laboratory indicators in the first trimester can provide a theoretical basis for early screening, monitoring, and intervention of GDM high-risk pregnant women.

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.