Gestational Diabetes Mellitus Model Research Articles

Placental Fatty Acid Metabolism and Transport in a Rat Model of Gestational Diabetes Mellitus.

Gestational diabetes mellitus (GDM) is a form of heightened insulin resistance triggered during gestation. This study examines how insulin resistance alters placental long-chain polyunsaturated fatty acid (LCPUFA) transport and metabolism in a rat model of lean GDM. Pregnant Sprague Dawley rats were administered with S961, an insulin receptor antagonist (30 nmol/kg s.c. daily), or vehicle from gestational day (GD) 7 to 20. Daily maternal body weight, food, and water intake were measured. Blood pressure assessment and glucose tolerance test were done on GD20. Fetal plasma and placenta were collected on GD20 and processed for fatty acid measurement using LC-mass spectrometry. The expression of fatty acid metabolism-related genes in the placenta was assessed using RT2 Profiler PCR arrays. The results were validated by qRT-PCR. Blockade of insulin receptors with S961 in pregnant rats resulted in glucose intolerance with increased fasting glucose and insulin levels. Maternal body weight gain and food and water intake were not affected; however, S961 significantly increased maternal blood pressure and heart rate. The placenta n3 and n6 LCPUFA concentrations were significantly decreased by 8% and 11%, respectively, but their levels in the fetal plasma were increased by 15% and 4%. RT2 profiler arrays revealed that placental expressions of 10 genes related to fatty acid β-oxidation (Acaa1a, Acadm, Acot2, Acox2, Acsbg1, Acsl4, Acsm5, Cpt1b, Eci2, Ehhadh) and 3 genes related to fatty acid transport pathway (Fabp2, Fabp3, Slc27a3) were significantly upregulated. In summary, lack of insulin action increased the expression of genes related to placental fatty acid β-oxidation and transport with an increased transfer of LCPUFA to the fetus. The increased lipid levels routed toward the fetus may lead to fat adiposity and later-life metabolic dysfunction.

PS-C17-6: MATERNAL ENDOTHELIAL MITOCHONDRIAL DYSFUNCTION IN GESTATIONAL DIABETES MELLITUS

Objective: Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes, and short and long-term maternal and neonatal cardiovascular risks. Our laboratory has shown that elevated levels of endothelial extracellular vesicles (EVs) in pregnant women is predictive of adverse pregnancy outcomes in type 1 diabetes. However, this matter has not been explored in GDM. We therefore investigated the impact of GDM on maternal vascular health, focusing on the mitochondria. Design and method: We determined the mitochondrial protein expression altered by GDM in rat kidneys by Western blot analysis. We examined the levels of circulating endothelial EVs and EV mitochondrial DNA (mtDNA) from rat plasma samples via flow cytometry and quantitative real-time PCR, respectively. We assessed the effects of high glucose exposure for 48 hours on endothelial mitochondrial function in human umbilical vein endothelial cells (HUVECs). Results: In a rat model of diet-induced GDM (Pereira, 2015 J Physiol), we observed significant decreases in expression of oxidative phosphorylation Complexes I (p < 0.05) and II (p < 0.01) in kidneys. We also observed a three-fold increase in late gestational levels of circulating endothelial EVs in GDM rats (p < 0.01) as well as increases in levels of EV-associated mtDNA, COX2 (p < 0.05) and ND2 (p < 0.01), suggesting mitochondrial dysfunction. In cultured HUVECs exposed to high glucose for 48 hours, we observed a significant decrease in Complex II expression (p < 0.01) and five-fold increases in EV mtDNA levels. This effect persisted even after removing high glucose exposure for 24 hours. Conclusion: These data show maternal endothelial dysfunction in GDM, accompanied by mitochondrial dysfunction and increased endothelial EV release. These findings suggest that endothelial mitochondrial health may represent a novel approach to reduce risks associated with GDM.

Polyphyllin I alleviates inflammatory injury in mice with gestational diabetes through AMPK pathway

Our study aimed to detect the effects of polyphyllin I (PPI) on relieving gestational diabetes mellitus (GDM), and the possible mechanism. A mouse model of GDM was constructed. The effects of PPI on GDM mice were evaluated by detecting blood glucose, insulin level, glucose tolerance test, and insulin tolerance test. The inflammation response in GDM and GDM+PPI group were evaluated by enzyme-linked immunosorbent assay (ELISA). The effect of PPI on the offspring of GDM mice was analyzed. In addition, immunoblot assays were performed to investigate the effects of PPI on the AMPK pathway. We found that PPI improved diabetes-related symptoms and decreased serum inflammatory response in GDM mice. In addition, we also found that PPI reduced the tissue damage of GDM mice. We noticed that PPI alleviated inflammatory injury in GDM mice through targeting AMPK pathway. Our findings showed that PPI has the potential to be explored as the drug for GDM treatment.

A novel model of gestational diabetes: Acute high fat high sugar diet results in insulin resistance and beta cell dysfunction during pregnancy in mice.

Gestational diabetes mellitus (GDM) affects 7-18% of all pregnancies. Despite its high prevalence, there is no widely accepted animal model. To address this, we recently developed a mouse model of GDM. The goal of this work was to further characterize this animal model by assessing insulin resistance and beta cell function. Mice were randomly assigned to either control (CD) or high fat, high sugar (HFHS) diet and mated 1 week later. At day 0 (day of mating) mice were fasted and intraperitoneal insulin tolerance tests (ipITT) were performed. Mice were then euthanized and pancreata were collected for histological analysis. Euglycemic hyperinsulinemic clamp experiments were performed on day 13.5 of pregnancy to assess insulin resistance. Beta cell function was assessed by glucose stimulated insulin secretion (GSIS) assay performed on day 0, 13.5 and 17.5 of pregnancy. At day 0, insulin tolerance and beta cell numbers were not different. At day 13.5, glucose infusion and disposal rates were significantly decreased (p<0.05) in Pregnant (P) HFHS animals (p<0.05) suggesting development of insulin resistance in P HFHS dams. Placental and fetal glucose uptake was significantly increased (p<0.01) in P HFHS dams at day 13.5 of pregnancy and by day 17.5 of pregnancy fetal weights were increased (p<0.05) in P HFHS dams compared to P CD dams. Basal and secreted insulin levels were increased in HFHS fed females at day 0, however at day 13.5 and 17.5 GSIS was decreased (p<0.05) in P HFHS dams. In conclusion, this animal model results in insulin resistance and beta cell dysfunction by mid-pregnancy further validating its relevance in studying the pathophysiology GDM.

GLP-1 rescued gestational diabetes mellitus-induced suppression of fetal thalamus development.

Diabetes can be classified as type 1, type 2, and gestational diabetes mellitus (GDM). It has been reported that children born from mothers with GDM present motor impairment, however, underlying mechanisms of GDM-induce fetal neurological diseases remain unknown. In this study, NOD (nonobese diabetic) mice were used to construct the GDM model; after 2 weeks of gestation, thalamocortical axon development of fetal was evaluated by immunofluorescence. PCR of LRRC4C was used to confirm axon development of the thalamus cortex. RNA array was used to predict possible targets affected by GDM during fetal neurodevelopment. Western blot was used to investigate the underlying mechanism, PI3K inhibitor, and MAPK inhibitor was used to determine key pathway involved in this model, in vitro axonal growth was evaluated using neural stem cells, tactile sensory behavior of offspring was assessed to confirm neurological influence further. The result shown that maternal diabetes significantly suppressed axonal development of fetal thalamus cortex, PCR array of GDM fetal brain indicated that upregulation of GLP-1R compared with normal fetal, ELISA confirmed that GLP-1 level was decreased in GDM maternal serum compared with that of wild type pregnant mice. In vitro study observed enhanced axonal elongation after supplements of GLP-1 analog, GLP-1 analog PI3K-dependently active ROCK1 activity, IP injection of GLP-1 analog could partly reverse GDM-induced suppression of fetal thalamocortical axon development and improve tactile sensory behavior of GDM offspring. Our study provided a novel mechanism of GDM induced-neurological diseases and predicted GLP-1 as possible prevention supplement during gestation.

Clinical First-Trimester Prediction Models for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis.

Gestational diabetes mellitus (GDM) is a common pregnancy complication that negatively impacts the health of both the mother and child. Early prediction of the risk of GDM may permit prompt and effective interventions. This systematic review and meta-analysis aimed to summarize the study characteristics, methodological quality, and model performance of first-trimester prediction model studies for GDM. Five electronic databases, one clinical trial register, and gray literature were searched from the inception date to March 19, 2022. Studies developing or validating a first-trimester prediction model for GDM were included. Two reviewers independently extracted data according to an established checklist and assessed the risk of bias by the Prediction Model Risk of Bias Assessment Tool (PROBAST). We used a random-effects model to perform a quantitative meta-analysis of the predictive power of models that were externally validated at least three times. We identified 43 model development studies, six model development and external validation studies, and five external validation-only studies. Body mass index, maternal age, and fasting plasma glucose were the most commonly included predictors across all models. Multiple estimates of performance measures were available for eight of the models. Summary estimates range from 0.68 to 0.78 (I2 ranged from 0% to 97%). Most studies were assessed as having a high overall risk of bias. Only eight prediction models for GDM have been externally validated at least three times. Future research needs to focus on updating and externally validating existing models.

Differences in lipid metabolism in acquired versus preexisting glucose intolerance during gestation: role of free fatty acids and sphingosine-1-phosphate

BackgroundThe prevalence of gestational diabetes mellitus (GDM) is increasing worldwide. There is increasing evidence that GDM is a heterogeneous disease with different subtypes. An important question in this context is whether impaired glucose tolerance (IGT), which is a typical feature of the disease, may already be present before pregnancy and manifestation of the disease. The latter type resembles in its clinical manifestation prediabetes that has not yet manifested as type 2 diabetes (T2DM). Altered lipid metabolism plays a crucial role in the disorder's pathophysiology. The aim was to investigate the role of lipids which are relevant in diabetes-like phenotypes in these both models with different time of initial onset of IGT.MethodsTwo rodent models reflecting different characteristics of human GDM were used to characterize changes in lipid metabolism occurring during gestation. Since the New Zealand obese (NZO)-mice already exhibit IGT before and during gestation, they served as a subtype model for GDM with preexisting IGT (preIGT) and were compared with C57BL/6 N mice with transient IGT acquired during gestation (aqIGT). While the latter model does not develop manifest diabetes even under metabolic stress conditions, the NZO mouse is prone to severe disease progression later in life. Metabolically healthy Naval Medical Research Institute (NMRI) mice served as controls.ResultsIn contrast to the aqIGT model, preIGT mice showed hyperlipidemia during gestation with elevated free fatty acids (FFA), triglycerides (TG), and increased atherogenic index. Interestingly, sphingomyelin (SM) concentrations in the liver decreased during gestation concomitantly with an increase in the sphingosine-1-phosphate (S1P) concentration in plasma. Further, preIGT mice showed impaired hepatic weight adjustment and alterations in hepatic FFA metabolism during gestation. This was accompanied by decreased expression of peroxisome proliferator-activated receptor alpha (PPARα) and lack of translocation of fatty acid translocase (FAT/CD36) to the hepatocellular plasma membrane.ConclusionThe preIGT model showed impaired lipid metabolism both in plasma and liver, as well as features of insulin resistance consistent with increased S1P concentrations, and in these characteristics, the preIGT model differs from the common GDM subtype with aqIGT. Thus, concomitantly elevated plasma FFA and S1P concentrations, in addition to general shifts in sphingolipid fractions, could be an interesting signal that the metabolic disorder existed before gestation and that future pregnancies require more intensive monitoring to avoid complications.Graphical This graphical abstract was created with BioRender.com.

AST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus.

BackgroundAspartate aminotransferase-to-alanine transaminase ratio (AST/ALT) has been reported affect the risk of type 2 diabetes (T2DM), but it is uncertain if it has relationship with gestational diabetes mellitus (GDM).ObjectivesOur study aimed to investigate the association between AST/ALT ratio in the first trimester and the risk of subsequent development of GDM.MethodThis prospective cohort study enrolling 870 pregnant women, 204 pregnant women with missing data or liver diseases were excluded, 666 pregnant women were included in this study containing 94 GDM women. Blood samples were collected in the first trimester. Univariate analysis and multivariate logistic regression were used to evaluate the association between AST/ALT and GDM. Nomogram was established based on the results of multivariate logistic analysis. Receiver Operating Characteristic (ROC) curves and calibration curves were used to evaluate the predictive ability of this nomogram model for GDM. Decision curve analysis (DCA) was used to examine the clinical net benefit of predictive model.ResultsAST/ALT ratio (RR:0.228; 95% CI:0.107-0.488) was associated with lower risk of GDM after adjusting for confounding factors. Indicators used in nomogram including AST/ALT, maternal age, preBMI, waist circumference, glucose, triglycerides, high density lipoprotein cholesterol and parity. The area under the ROC curve (AUC) value of this predictive model was 0.778, 95% CI (0.724, 0.832). Calibration curves for GDM probabilities showed acceptable agreement between nomogram predictions and observations. The DCA curve demonstrated a good positive net benefit in the predictive model.ConclusionsThe early AST/ALT level of pregnant women negatively correlated with the risk of GDM. The nomogram including AST/ALT at early pregnancy shows good predictive ability for the occurrence of GDM.

Construction of the experimental rat model of gestational diabetes.

Numerous methods for modeling gestational diabetes mellitus (GDM) in rats exist. However, their repeatability and stability are unclear. This study aimed to compare the effects of high-fat and high-sugar (HFHS) diet, HFHS diet combined with streptozotocin (STZ) administration, and HFHS diet combined with movement restriction (MR) modeling methods on rat models to confirm the best method for constructing a rat model of GDM. Forty female Sprague-Dawley rats were randomly divided into four groups (n = 10): the normal control (NC), HFHS, HFHS+STZ, and HFHS+MR groups. The rats in the NC group were fed with a standard diet, and those in the remaining groups were fed with a HFHS diet. The rats in the HFHS+STZ group received 25 mg/kg STZ on their first day of pregnancy, and those in the HFHS+MR group were subjected to MR during pregnancy. Bodyweight, food intake, water intake, fasting blood glucose (FBG), fasting insulin (FINS), homeostasis model assessment of insulin resistance (HOMA-IR), homeostasis model assessment of insulin sensitivity (HOMA-IS), homeostasis model assessment of β-cell function, pancreatic and placental morphology, and the expression levels of glucose transporter 1 (GLUT1) and glucose transporter 3 (GLUT3) in placentas were then quantified. Moreover, iTRAQ was used to identify placental proteomics. During pregnancy, the rats in the HFHS+STZ group showed FBG levels that were kept stable in a state of moderate hyperglycemia; the typical GDM symptoms of polydipsia, polyphagia, polyuria, and increased body weight; and the modeling rate of 87.5%. On the first and 19th days of pregnancy, the rats in the HFHS group showed higher FBG than that of the NC group, increasing body weight and food intake and the modeling rate of 50%. On the 19th day of pregnancy, the FBG of the rats in the HFHS+MR group was higher than that of the rats in the NC group, and the modeling rate of 42.9%. Comparison with the NC group revealed that the three modeling groups exhibited increased FINS and HOMA-IR, decreased HOMA-IS, and different degrees of pathological changes in pancreases and placentas. Among the groups, the HFHS+STZ group displayed the greatest changes with significant reductions in the numbers of pancreatic and placental cells and appeared cavitation. The expression levels of GLUT1 and GLUT3 in the placentas of the HFHS+STZ and HFHS+MR groups were higher than those in the placentas of the NC and HFHS groups. The above results indicated that the rats in the HFHS+STZ group showed the best performance in terms of modeling indicators. After the changes in placental proteomics in the HFHS+STZ group were compared with those in the NC group, we found that in the HFHS+STZ group, five proteins were up-regulated and 18 were down-regulated; these proteins were enriched in estrogen signaling pathways. HFHS combined with the intraperitoneal injection of 25 mg/kg STZ was the best modeling method for the nonspontaneous model of experimentally induced GDM, and its modeling rate was high. The pathological characteristics of the constructed GDM rat model were similar to those of human patients with GDM. Moreover, the model was stable and reliable. The modeling method can provide a basis for constructing a GDM rat model for subsequent research on the prevention and treatment of GDM.

Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications

We assessed the diagnostic potential of cardiovascular disease-associated microRNAs for the early prediction of gestational diabetes mellitus (GDM) in singleton pregnancies of Caucasian descent in the absence of other pregnancy-related complications. Whole peripheral venous blood samples were collected within 10 to 13 weeks of gestation. This retrospective study involved all pregnancies diagnosed with only GDM (n = 121) and 80 normal term pregnancies selected with regard to equality of sample storage time. Gene expression of 29 microRNAs was assessed using real-time RT-PCR. Upregulation of 11 microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-181a-5p, miR-195-5p, miR-499a-5p, and miR-574-3p) was observed in pregnancies destinated to develop GDM. Combined screening of all 11 dysregulated microRNAs showed the highest accuracy for the early identification of pregnancies destinated to develop GDM. This screening identified 47.93% of GDM pregnancies at a 10.0% false positive rate (FPR). The predictive model for GDM based on aberrant microRNA expression profile was further improved via the implementation of clinical characteristics (maternal age and BMI at early stages of gestation and an infertility treatment by assisted reproductive technology). Following this, 69.17% of GDM pregnancies were identified at a 10.0% FPR. The effective prediction model specifically for severe GDM requiring administration of therapy involved using a combination of these three clinical characteristics and three microRNA biomarkers (miR-20a-5p, miR-20b-5p, and miR-195-5p). This model identified 78.95% of cases at a 10.0% FPR. The effective prediction model for GDM managed by diet only required the involvement of these three clinical characteristics and eight microRNA biomarkers (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, and miR-574-3p). With this, the model identified 50.50% of GDM pregnancies managed by diet only at a 10.0% FPR. When other clinical variables such as history of miscarriage, the presence of trombophilic gene mutations, positive first-trimester screening for preeclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm, and family history of diabetes mellitus in first-degree relatives were included in the GDM prediction model, the predictive power was further increased at a 10.0% FPR (72.50% GDM in total, 89.47% GDM requiring therapy, and 56.44% GDM managed by diet only). Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for GDM.

Development, validation and clinical utility of a risk prediction model for adverse pregnancy outcomes in women with gestational diabetes: The PeRSonal GDM model.

SummaryBackgroundThe ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM.MethodsA prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis.FindingsThe final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination (c-statistic 0.68; 95% CI 0.64 to 0.72) when temporally validated. Decision curve analysis demonstrated that the model was useful across a range of predicted probability thresholds between 0.15 and 0.85 for adverse pregnancy outcomes compared to the alternatives of managing all women with GDM as if they will or will not have an adverse pregnancy outcome.InterpretationThe PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria.FundingThis work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847.

β-Carotene regulates glucose transport and insulin resistance in gestational diabetes mellitus by increasing the expression of SHBG.

The aim of this work was to study the effect and mechanism of β-carotene on insulin resistance and glucose transport in gestational diabetes mellitus (GDM). Placental tissue and venous blood of 26 GDM patients and 18 normal women were collected. Mice fed a high-fat diet were established as GDM models and treated with β-carotene, from which peripheral blood and placenta tissue were collected. HTR-8/SVneo cells were treated with 10-7 mol/L insulin for 48 h and established as insulin resistance cell models. The expression of SHBG, GLUT1, GLUT3, GLUT4, IRS-1, IRS-2, PI3Kp85α, and p-CREB/CREB in placental tissues and HTR-8/SVneo cells was detected. Insulin resistance index was calculated from the values of fasting blood glucose and fasting insulin. The glucose consumption of insulin-resistant cells was calculated by detecting the glucose content of the supernatant. The cyclic adenosine monophosphate (cAMP) kit was applied to measure the concentration of cAMP in cells. SHBG was lowly expressed in GDM. β-Carotene treatment upregulated SHBG in the placenta of GDM mice and in insulin-resistant HTR-8/SVneo cells. Overexpression of SHBG upregulated GLUT3, GLUT4, and IRS-1 and enhanced glucose consumption in insulin-resistant cells. β-Carotene treatment promoted the expression of SHBG, GLUT4 and IRS-1 and increased glucose consumption in insulin-resistant cells underexpressing SHBG. Silencing of SHBG decreased the levels of cAMP and pCREB/CREB but β-carotene treatment increased their expression despite SHBG silencing in insulin-resistant cells. β-Carotene promotes glucose transport and inhibits insulin resistance in GDM by increasing the expression of SHBG.

IRF2BP2 attenuates gestational diabetes mellitus by activating AMPK signaling

Purpose: To investigate the role of interferon regulatory factor 2 binding protein 2 (IRF2BP2) in gestational diabetes mellitus (GDM).&#x0D; Methods: Mice were injected intraperitoneally with streptozotocin to establish a model of GDM and then subjected to intraperitoneal glucose tolerance test (IPGTT) and intraperitoneal insulin tolerance test (IPITT) to determine glucose and insulin tolerances. Lipid metabolism was evaluated by enzyme-linked immunosorbent assay (ELISA). The histomorphology of pancreatic islets was assessed by hematoxylin and eosin staining.&#x0D; Results: IRF2BP2 was downregulated in pancreatic tissues of mice with GDM (p &lt; 0.001). Mice in GDM group showed higher blood glucose levels than those in normal pregnancy group. However, overexpression of IRF2BP2 reduced glucose and insulin levels in mice with GDM. Overexpression of IRF2BP2 increased the level of high-density lipoprotein (HDL) and reduced triglyceride (TG), total cholesterol (TC), and low-density lipoprotein (LDL) levels in mice with GDM (p &lt; 0.001). The histopathological changes in the islets of mice with GDM were also ameliorated by overexpression of IRF2BP2. Overexpression of IRF2BP2 reduced IL-6, IL-1β, and TNF-α levels and increased protein expression of p-AMPK in mice with GDM.&#x0D; Conclusion: IRF2BP2 ameliorates the outcomes of GDM and suppressed inflammation in mice with GDM through activation of AMPK signaling. Thus, IRF2BP2 is a potential therapeutic strategy for the management of GDM.&#x0D; Keywords: IRF2BP2; inflammation; gestational diabetes mellitus; AMPK; mice; insulin tolerance

Selenium Prevents Inflammation in Human Placenta and Adipose Tissue In Vitro: Implications for Metabolic Diseases of Pregnancy Associated with Inflammation.

Gestational diabetes mellitus (GDM) and maternal obesity are significant metabolic complications increasingly prevalent in pregnancy. Of major concern, both GDM and maternal obesity can have long-term detrimental impacts on the health of both mother and offspring. Recent research has shown that increased inflammation and oxidative stress are two features central to the pathophysiology of these metabolic conditions. Evidence suggests selenium supplementation may be linked to disease prevention in pregnancy; however, the specific effects of selenium on inflammation and oxidative stress associated with GDM and maternal obesity are unknown. Therefore, this study aimed to investigate the effect of selenium supplementation on an in vitro model of GDM and maternal obesity. Human placental tissue, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were stimulated with either the bacterial product lipopolysaccharide (LPS) or the pro-inflammatory cytokine TNF-α. Selenium pre-treatment blocked LPS and TNF-α induced mRNA expression and secretion of pro-inflammatory cytokines and chemokines, while increasing anti-inflammatory cytokine and antioxidant mRNA expression in placenta, VAT and SAT. Selenium pre-treatment was also found to inhibit LPS- and TNF-α induced phosphorylation of ERK in placenta, VAT and SAT. These findings indicate that selenium may be able to prevent inflammation and oxidative stress associated with GDM and maternal obesity. Additional in vivo studies are required to identify the efficacy of selenium supplementation in preventing inflammatory pathways activated by GDM and maternal obesity and to elucidate the mechanism involved.

Dynamic regulation of lipid metabolism in the placenta of in vitro and in vivo models of gestational diabetes mellitus†.

The purpose of this study was to investigate lipid metabolism in the placenta of gestational diabetes mellitus individuals and to evaluate its effect on the fetus. We examined the expression of lipogenesis- and lipolysis-related proteins in the in vitro and in vivo gestational diabetes mellitus placenta models. The levels of sterol regulatory element binding protein-1c were increased, and fat accumulated more during early hyperglycemia, indicating that lipogenesis was stimulated. When hyperglycemia was further extended, lipolysis was activated due to the phosphorylation of hormone-sensitive lipase and expression of adipose triglyceride lipase. In the animal model of gestational diabetes mellitus and in the placenta of gestational diabetes mellitus patients during the extended stage of gestational diabetes mellitus, the expression of sterol regulatory element binding protein-1c decreased and the deposition of fat increased. Similar to the results obtained in the in vitro study, lipolysis was enhanced in the animal and human placenta of extended gestational diabetes mellitus. These results suggest that fat synthesis may be stimulated by lipogenesis in the placenta when the blood glucose level is high. Subsequently, the accumulated fat can be degraded by lipolysis and more fat and its metabolites can be delivered to the fetus when the gestational diabetes mellitus condition is extended at the late stage of gestation. Imbalanced fat metabolism in the placenta and fetus of gestational diabetes mellitus patients can cause metabolic complications in the fetus, including fetal macrosomia, obesity, and type 2 diabetes mellitus.

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

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

A clinical model and nomogram for early prediction of gestational diabetes based on common maternal demographics and routine clinical parameters.

We aimed to develop a risk prediction model for gestational diabetes mellitus (GDM) based on the common maternal demographics and routine clinical variables in Chinese population. Individual information was collected from December 2018 to October 2019 by a pretested questionnaire on demographics, medical and family history, and lifestyle factors. Multivariable logistic regression was performed to establish a predictive model for GDM by variables in pre- and early pregnancy. The consistency and discriminative validity of the model were evaluated by Hosmer-Lemeshow goodness-of-fit testing and ROC curve analysis. Internal validation was appraised by fivefold cross-validation. Clinical utility was assessed by decision curve analysis. Total 3263 pregnant women were included with 17.2% prevalence of GDM. The model equation was: LogitP = -11.432 + 0.065 × maternal age (years) + 0.061 × pre-pregnancy BMI (kg/m2 ) + 0.055 × weight gain in early pregnancy (kg) + 0.872 × history of GDM + 0.336 × first-degree family history of diabetes +0.213 × sex hormone usages during pre- or early pregnancy + 1.089 × fasting glucose (mmol/L) + 0.409 × triglycerides (mmol/L) + 0.082 × white blood cell count (109/L) + 0.669 × positive urinary glucose. Homer-Lemeshow goodness-of-fit testing indicated a good consistency between predictive and actual data (p = 0.586). The area under the ROC curve (AUC) was 0.720 (95% CI: 0.697 ~ 0.744). Cross-validation suggested a good internal validity of the model. A nomogram has been made to establish an easy to use scoring system for clinical practice. The predictive model of GDM exhibited well acceptable predictive ability, discriminative performance, and clinical utilities. The project was registered in clinicaltrial.gov.com with identifier of NCT03922087.

Orphan nuclear receptor NUR77 relieves insulin resistance in HTR-8/SVneo trophoblast cells through activation of autophagy and insulin signaling.

Gestational diabetes mellitus (GDM) is a common pregnancy complication with a high incidence in women. Orphan nuclear receptor NUR77 is involved in regulating glucose metabolism. However, its role in GDM has not been fully elucidated yet. In this study, an animal model of GDM was established by feeding mice with a high-fat diet (HFD) before and during pregnancy. NUR77 expression was abnormally upregulated in placenta tissues of GDM mice. We performed gain- and loss-of-function studies of NUR77 in HTR-8/SVneo cells. Cells were incubated with 1 × 10-6M insulin for 48h to induce insulin resistance (IR). The expression of NUR77 was downregulated in HTR-8/SVneo cells following IR induction. Overexpression of NUR77 promoted cell proliferation, migration, and invasion. Notably, NUR77 promoted glucose uptake and enhanced insulin sensitivity in vitro. NUR77 increased the ratio of p-insulin receptor β (IRβ)Tyr1361/IRβ, p-insulin receptor substrate (IRS)-1Tyr612/IRS-1, p-Akt/Akt and decreased p-IRS-1Ser307/IRS-1, as well as lowered the expression of glucose transport protein type 1 (GLUT1) and elevated GLUT4. These results suggest the involvement of IRβ/IRS/Akt/GLUT4 signaling activation in the regulatory effects of NUR77 on IR in HTR-8/SVneo cells. Silencing of NUR77 displayed opposite effects. Besides, NUR77 enhanced the expression of autophagy-related protein Beclin 1 and the ratio of LC3II/LC3I. Further study demonstrated that the inhibitory effect of NUR77 on IR was partially attributed to the activation of autophagy. Therefore, we demonstrate that NUR77 enhances insulin sensitivity in HTR-8/SVneo cells likely through activating IRβ/IRS/Akt/GLUT4 pathway and regulating autophagy.

Machine Learning-Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study.

BackgroundThe increasing prevalence of gestational diabetes mellitus (GDM) is concerning as women with GDM are at high risk of type 2 diabetes (T2D) later in life. The magnitude of this risk highlights the importance of early intervention to prevent the progression of GDM to T2D. Rates of postpartum screening are suboptimal, often as low as 13% in Asian countries. The lack of preventive care through structured postpartum screening in several health care systems and low public awareness are key barriers to postpartum diabetes screening.ObjectiveIn this study, we developed a machine learning model for early prediction of postpartum T2D following routine antenatal GDM screening. The early prediction of postpartum T2D during prenatal care would enable the implementation of effective strategies for diabetes prevention interventions. To our best knowledge, this is the first study that uses machine learning for postpartum T2D risk assessment in antenatal populations of Asian origin.MethodsProspective multiethnic data (Chinese, Malay, and Indian ethnicities) from 561 pregnancies in Singapore’s most deeply phenotyped mother-offspring cohort study—Growing Up in Singapore Towards healthy Outcomes—were used for predictive modeling. The feature variables included were demographics, medical or obstetric history, physical measures, lifestyle information, and GDM diagnosis. Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters.ResultsA high performance prediction model for postpartum T2D comprising of 2 midgestation features—midpregnancy BMI after gestational weight gain and diagnosis of GDM—was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P=.02; OR 0.88, 95% CI 0.79-0.98).ConclusionsMidgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care.Trial RegistrationClinicalTrials.gov NCT01174875; https://clinicaltrials.gov/ct2/show/NCT01174875

Elevated blood pressure in pregnant women with gestational diabetes according to the WHO criteria: importance of overweight.

Hypertension before and during early pregnancy has been associated with an increased risk of gestational diabetes mellitus (GDM) in retrospective analyses. We aimed to investigate the prospective blood pressure trackings in a population-based cohort of pregnant women, who were stratified according to their metabolic status in early third trimester. We recorded blood pressure longitudinally during pregnancy in 1230 women from the Odense Child Cohort, Denmark. Fasting glucose and insulin were measured at gestational weeks 28-30. Metabolic status was evaluated according to the WHO 2013 threshold for GDM (GDM-WHO: fasting plasma glucose ≥5.1 mmol/l), insulin and homeostatic model assessment of insulin resistance (HOMA-IR). Relationships between metabolic status in third trimester and blood pressure trajectories were evaluated with adjusted linear mixed models. Trajectory was defined as blood pressure records in pregnancy per 4 weeks interval. Prevalence of GDM-WHO was 40% (498/1230). GDM-WHO was associated with 1.46 (0.22-2.70) mmHg higher SBP and 1.04 (0.07-2.01) mmHg higher DBP trajectories in the overall cohort. The associations were driven by differences in the overweight group, with 3.14 (1.05-5.25) mmHg higher SBP and 1.94 (0.42-3.47) mmHg higher DBP per 4 weeks in women with GDM-WHO compared with women without GDM-WHO. GDM-WHO was not associated with blood pressure in women with normal weight. Blood pressure trajectories were elevated across quartiles of insulin resistance. GDM-WHO is associated with higher blood pressure in pregnancy, and there appears to be a stronger effect in overweight women.