Gestational Diabetes Mellitus Model Research Articles

Abstract 4139148: Mechanisms of Maternal Cardiovascular Disease Development in a Novel Mouse Model of Pregnancy-Induced Diabetes

Background: Women with gestational diabetes mellitus (GDM) are more likely to develop cardiovascular disease (CVD) within 3 to 10 years following pregnancy. Endothelial dysfunction is a mechanism implicated in both hyperglycemia and CVDs, and it may constitute the missing link in this interaction. One reason for the lack of progress into the cardiovascular complications of GDM is the need for appropriate preclinical models that mimic human GDM. Also, the exact mechanisms mediating increased future cardiovascular risk in maternal with pregnancy-induced diabetes is unclear. Aim: To determine the mechanisms underlying cardiovascular disease in hormonal model of GDM. Methods: Sixteen week-old C57BL/6J female mice were treated with insulin receptor antagonist (S961) or saline (n=4) from the second trimester of pregnancy until delivery to develop a model of GDM. Glucose tolerance test was determined by glucometer and fasting insulin by colorimetry. Cardiac structure and function were measured by echocardiography (VEVO 3100) and pulse wave velocity was measured by Pulse Wave Doppler. Exercise tolerance test was performed on treadmill. Plasma endothelin-1 and nitrite were measured by ELISA and colorimetric assay. Aortic SIRT-1 was determined by western blot and aortic vascular reactivity was determined by wire myograph. Results: S961-induced GDM mice exhibit hyperglycemia (169.2 ± 3.6 vs. 105.5 ± 10.8mg/dL, P<0.004 ) and hyperinsulinemia (1.9 ± 0.2 vs. 0.8 ± 0.1ng/ml , p<0.01 ) which mimics the condition observed in human GDM. Interestingly, S961-induced GDM mice went on to develop systolic and diastolic dysfunction, vascular stiffness (3.7 ± 0.1 vs. 3.1 ± 0.08m/s, p<0.005 ), exercise intolerance and increased thickness of the cardiac wall at 10 weeks post-delivery. Plasma ET-1 was elevated in our model during pregnancy (28.1 ± 1.7 vs. 5.2 ± 0.2pg/ml, p<0.002 ) and at 10 weeks post-delivery (19.9 ± 2.9 vs. 6.7 ± 0.7pg/ml, p<0.05 ). Circulating NO and aortic expression of sirtuin-1 (SIRT 1), that is involved in endothelial longevity is reduced in GDM mice. Additionally, vascular relaxation to acetylcholine was attenuated in S961-induced GDM mice. Conclusion: We have developed a novel mouse model of GDM similar to that observed in pregnant women by treatment with S961. Moreover, S961 treated mice develop cardiac dysfunction and vascular stiffness at 10 weeks post-delivery. Alterations in ET-1, SIRT 1, and NO levels may be responsible for the development of CVD in this model.

Lingguizhugan decoction alleviates gestational diabetes mellitus by modulating the PI3K-AKT pathway and oxidative stress: Network pharmacology and experimental evidence.

The Lingguizhugan decoction (LGZGD) is a promising traditional Chinese medicine for the treatment of gestational diabetes mellitus (GDM). However, its bioactive compounds and therapeutic mechanisms remain unknown. The main chemical composition of LGZGD was analyzed by high-performance liquid chromatography-mass spectrometry (HPLC-MS). Furthermore, the underlying mechanisms of LGZGD against GDM were elucidated through network pharmacology and molecular docking. The therapeutic efficacy and targets of LGZGD were further confirmed via an in vitro GDM model (high glucose [HG]-treated HTR-8/SVneo cells). Four compounds of LGZGD, namely, cinnamaldehyde, glycyrrhizic acid, 2-atractylenolide, and pachymic acid, were detected. A total of 26 targets for LGZGD treating GDM were obtained, which were mainly involved in oxidative stress and the PI3K-AKT signaling pathway. The protein-protein interaction (PPI) network unveiled that AKT1, TLR4, TP53, and NOS3 were hub therapeutic targets. Molecular docking showed that these targets had strong affinity with key compounds. In vitro experiments confirmed that LGZGD treatment promoted HG-induced cell viability, migration, and invasion ability while inhibited the apoptosis rate and oxidative stress. Mechanically, western blot revealed that LGZGD may protect HG-treated cells by activating the PI3K-AKT pathway and suppressing TLR4 expression. Our study preliminarily explored the mechanism of LGZGD in GDM treatment, providing a scientific basis for the clinical application of LGZGD.

First-Trimester Prediction Models Based on Maternal Characteristics for Adverse Pregnancy Outcomes: A Systematic Review and Meta-Analysis.

Early risk stratification can facilitate timely interventions for adverse pregnancy outcomes, including preeclampsia (PE), small-for-gestational-age neonates (SGA), spontaneous preterm birth (sPTB) and gestational diabetes mellitus (GDM). To perform a systematic review and meta-analysis of first-trimester prediction models for adverse pregnancy outcomes. The PubMed database was searched until 6 June 2024. First-trimester prediction models based on maternal characteristics were included. Articles reporting on prediction models that comprised biochemical or ultrasound markers were excluded. Two authors identified articles, extracted data and assessed risk of bias and applicability using PROBAST. A total of 77 articles were included, comprising 30 developed models for PE, 15 for SGA, 11 for sPTB and 35 for GDM. Discriminatory performance in terms of median area under the curve (AUC) of these models was 0.75 [IQR 0.69-0.78] for PE models, 0.62 [0.60-0.71] for SGA models of nulliparous women, 0.74 [0.72-0.74] for SGA models of multiparous women, 0.65 [0.61-0.67] for sPTB models of nulliparous women, 0.71 [0.68-0.74] for sPTB models of multiparous women and 0.71 [0.67-0.76] for GDM models. Internal validation was performed in 40/91 (43.9%) of the models. Model calibration was reported in 21/91 (23.1%) models. External validation was performed a total of 96 times in 45/91 (49.5%) of the models. High risk of bias was observed in 94.5% of the developed models and in 58.3% of the external validations. Multiple first-trimester prediction models are available, but almost all suffer from high risk of bias, and internal and external validations were often not performed. Hence, methodological quality improvement and assessment of the clinical utility are needed.

Nonlinear association between alanine aminotransferase to high-density lipoprotein cholesterol ratio and risk of gestational diabetes mellitus

Previous studies have indicated a potential association between the alanine aminotransferase to high-density lipoprotein cholesterol (ALT/HDL-C) ratio and the risk of Type 2 Diabetes Mellitus, but its relation with gestational diabetes mellitus (GDM) remains uncertain. This study aims to investigate the correlation between the ALT/HDL-C ratio in early pregnancy and the risk of GDM. This study is a secondary analysis based on an open-source cohort study. A total of 590 single pregnant women attending two hospitals in Korea up to 14 weeks gestation were included between November 2014 and July 2016. Logistic regression analysis, subgroup analysis, and smooth curve fitting were employed to explore the association between the ALT/HDL-C ratio and GDM risk. The predictive capability of the ALT/HDL-C ratio for GDM was assessed using ROC curve analysis. The average age of participants was 32.06 ± 3.80 years, with a GDM incidence rate of 6.27%. Multifactorial logistic regression analysis revealed that the serum ALT/HDL-C ratio is an independent influencing factor for GDM (OR = 1.08, 95% CI: 1.02–1.16). Furthermore, a non-linear relationship between the ALT/HDL-C ratio and GDM risk was observed, with a turning point at 5.51. The effect size (OR) on the left and right sides of the turning point were 0.75 (95% CI: 0.37–1.59) and 1.55 (95% CI: 1.18-2.00), respectively. Additionally, when combined with age, pre-pregnancy body mass index, parity, and insulin resistance index in a prediction model for GDM, the ALT/HDL-C ratio demonstrated improved sensitivity of prediction by reaching up to 67.6%, specificity of prediction by reaching up to 87.3%, and an area under curve value of 0.819 (95%CI: 0.743–0.894). In early pregnancy, the serum ALT/HDL-C ratio shows a positive correlation with maternal risk in a nonlinear manner. The combination of ALT/HDL-C ratio with maternal characteristics and metabolic indicators provides good predictive value for GDM. This study may facilitate optimization of GDM prevention in pregnant women and enable timely and effective intervention to enhance their prognosis.

Gestational diabetes exacerbates intrauterine microbial exposure induced intestinal microbiota change in offspring contributing to increased immune response.

maternal health during pregnancy can affect the intestinal microbial community of offspring, but currently the impact of intrauterine environmental changes resulting from gestational diabetes mellitus (GDM) on the microbiota of offspring as well as its interaction with the immune system remains unclear. to explore the impact of intrauterine microbial exposure during pregnancy of gestational diabetes mellitus on the development of neonate's intestinal microbiota and activation of immune responses. Levels of lipopolysaccharides in cord blood from GDM and expression of microbial recognition-related proteins in the placenta were measured. To evaluate embryonic intestinal colonization, pregnant mice with GDM were administered with labeled Escherichia coli or Lactobacillus. The intestinal colonization of pups was analyzed through 16S rRNA gene sequencing and labeled microbial culture. Additionally, memory T lymphocyte and dendritic cell co-culture experiments were conducted to elucidate the immune memory of intestinal microbes during the embryonic stages. Gestational diabetes mellitus led to elevated umbilical cord blood LPS level and increased GFP labeled Escherichia coli in the offspring's intestine after gestational microbial exposure. The mouse model of GDM exhibited increased immune markers including TLR4, TLR5, IL-22 and IL-23 in the placenta and a recall response from memory T cells in offspring's intestines, with similar observations found in human experiments. Furthermore, reduced intestinal microbiome diversity and an increased ratio of Firmicutes/Bacteroidetes was found in GDM progeny, with the stability of bacterial colonization been interfered. Our investigation has revealed a noteworthy correlation between gestational diabetes and intrauterine microbial exposure, as well as alterations in the neonatal microbiota and activation of immune responses. These findings highlight the gestational diabetes's role on offspring's gut microbiota and immune system interactions with early-life pathogen exposure.

A prediction model for gestational diabetes mellitus based on steroid hormonal changes in early and mid-down syndrome screening: A multicenter longitudinal study

BackgroundSteroid hormones (SH) during pregnancy are associated with the development of gestational diabetes mellitus (GDM). Early and mid-Down syndrome screening is used to assess the risk of Down syndrome in the fetus. It is unclear whether changes in SH during this period can be used as an early predictor of GDM. MethodsThis study was a multicenter, longitudinal cohort study. GDM is diagnosed by an oral glucose tolerance test (OGTT) between 24 and 28 weeks of gestation. We measured SH levels at early and mid-Down syndrome screening, respectively. Based on the SH changes, logistic regression analysis was used to construct a prediction model for GDM. Finally, evaluated the model’s predictive performance by creating a receiver operating characteristic curve (ROC) and performing external validation. ResultsThis study enrolled 193 pregnant women (discovery cohort, n = 157; validation cohort, n = 36). SH changes occur dynamically after pregnancy. At early Down syndrome screening, only cortisol (F) (p < 0.05, 95 % CI 4780.95–46083.68) was elevated in GDM. At mid-Down syndrome screening, free testosterone (FT) (p < 0.01, 95 % CI 0.10–0.55) and estradiol (E2) (p < 0.05, 95 % CI 203.55–1784.78) were also significantly elevated. There were significant differences in the rates of change in E2 (Fold change (FC) = 1.3425, p = 0.0072), albumin (ALB) (FC=1.5759, p = 0.0117), and dihydrotestosterone (DHT) (FC=-2.1234, p = 0.0165) between GDM and no-GDM. Stepwise logistic regression analysis resulted in the best predictive model, including six variables (Δweight, ΔF, Δcortisone (E), ΔE2, Δprogesterone (P), ΔDHT). The area under the curve for this model was 0.791, and for the external validation cohort, it was 0.799. ConclusionsA GDM prediction model can be constructed using SH measures during early and mid-Down syndrome screening.

The early prediction of gestational diabetes mellitus by machine learning models

BackgroundWe aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period.MethodsWe collected the data of pregnant women who were admitted to the obstetric clinic in the first trimester. The maternal age, body mass index, gravida, parity, previous birth weight, smoking status, the first-visit venous plasma glucose level, the family history of diabetes mellitus, and the results of an oral glucose tolerance test of the patients were evaluated. The women were categorized into groups based on having and not having a GDM diagnosis and also as being nulliparous or primiparous. 7 common ML algorithms were employed to construct the predictive model.Results97 mothers were included in the study. 19 and 26 nulliparous were with and without GDM, respectively. 29 and 23 primiparous were with and without GDM, respectively. It was found that the greatest feature importance variables were the venous plasma glucose level, maternal BMI, and the family history of diabetes mellitus. The eXtreme Gradient Boosting (XGB) Classifier had the best predictive value for the two models with the accuracy of 66.7% and 72.7%, respectively.DiscussionThe XGB classifier model constructed with maternal sociodemographic findings and the obstetric history could be used as an early prediction model for GDM especially in low-income countries.

Postpartum development of metabolic dysfunction-associated steatotic liver disease in a lean mouse model of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is associated with increased postpartum risk for metabolic dysfunction-associated steatotic liver disease (MASLD). GDM-related MASLD predisposes to advanced liver disease, necessitating a better understanding of its development in GDM. This preclinical study evaluated the MASLD development in a lean GDM mouse model with impaired insulin secretion capacity. Lean GDM was induced by short-term 60% high-fat diet and low-dose streptozotocin injections (60 mg/kg for 3 days) before mating in C57BL/6N mice. The control dams received only high-fat diet or low-fat diet. Glucose homeostasis was assessed during pregnancy and postpartum, whereas MASLD was assessed on postpartum day 30 (PP30). GDM dams exhibited a transient hyperglycemic phenotype during pregnancy, with hyperglycaemia reappearing after lactation. Lower insulin levels and impaired glucose-induced insulin response were observed in GDM mice during pregnancy and postpartum. At PP30, GDM dams displayed higher hepatic triglyceride content compared controls, along with increased MAS (MASLD) activity scores, indicating lipid accumulation, inflammation, and cell turnover indices. Additionally, at PP30, GDM dams showed elevated plasma liver injury markers. Given the absence of obesity in this double-hit GDM model, the results clearly indicate that impaired insulin secretion driven pregnancy hyperglycaemia has a distinct contribution to the development of postpartum MASLD.

Effects of asiaticoside on the model of gestational diabetes mellitus in HTR-8/svneo cells via PI3K/AKT pathway

Background Asiaticoside (AS) has been reported to improve the changes induced by high glucose stimulation, and it may have potential therapeutic effects on gestational diabetes mellitus (GDM). This study aims to explore the effect of AS on the cell model of GDM and the action mechanism of the PI3K/AKT pathway. Methods The GDM model was established in HTR-8/Svneo cells with a high glucose (HG) medium. After the cytotoxicity assay of AS, cells were divided into the control group, HG group and HG + AS group to conduct control experiment in cells. The cell proliferation and migration were detected by CCK-8 assay and scratch test, respectively. The mRNA levels of PI3K, AKT2, mTORC1, and GLUT4 in PI3K/AKT signalling pathway were measured by RT-PCR, and the protein expressions of these signalling molecules were monitored by western blot. Results AS showed a promotion effect on the cell proliferation rate of HTR-8/Svneo cells, and 80 μmol/L AS with a treatment time of 48 h had no cytotoxicity. The cell proliferation rate, migration rate, mRNA levels and protein expressions of PI3K, AKT2, mTORC1, and GLUT4 in the HG group were significantly lower than those in the control group, which were significantly increased in the HG + AS group (p < 0.05). Conclusions AS can facilitate the cell proliferation and migration in the cell model of GDM, and might play a role in GDM treatment via PI3K/AKT pathway.

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.

G protein-coupled receptor 1 participating in the mechanism of mediating gestational diabetes mellitus by phosphorylating the AKT pathway.

Gestational diabetes mellitus (GDM) is a metabolic disease that occurs during pregnancy. Herein, we investigate G protein-coupled receptor 1 (GPR1) in mediating GDM through the phosphorylation of serine/threonine kinase (AKT) pathway. Thirty pregnant SD rats were grouped into: normal pregnancy control group (NC), GDM model group, and GDM model + high-dose GPR1 antagonist treatment (GDM + Ari) group. GDM model was established, and the GDM + Ari group adopted GPR1 antagonist aripiprazole. The blood glucose level, insulin level, and insulin resistance (IR) were detected. The expression and phosphorylation of GPR1, AKT, and extracellular signal-regulated kinase (ERK) in placental tissue were detected using reverse transcription-polymerase chain reaction (RT-PCR) and western blotting (WB). The serum insulin concentration, glucose concentration, and glycated hemoglobin concentration during pregnancy in GDM group SD rats were significantly higher than those in the NC group (P < 0.05). The expression and phosphorylation levels of GPR1, AKT, and ERK in the placental tissue of SD pregnant rats in the GDM group were significantly lower than those in the NC group. Furthermore, compared with the GDM group, the expression of GPR1, AKT, and ERK in placental tissue was significantly reduced in the GDM + Ari group, while simultaneously enhancing the blood glucose level and IR level. In addition, the survival number, body weight, and malformation rate of the offspring of the GDM + Ari group were significantly improved, and there was no significant effect on the number of offspring. The expressions of GPR1, AKT, and ERK in placental tissue exhibited a significant decrease, while the glucose level and IR were observed to increase in the GDM + Ari group. Enhancing the expression of GPR1 may activate AKT phosphorylation to alleviate GDM. GPR1 could potentially serve as a novel target for diabetes treatment, offering new insights into managing GDM.

Demand and Requirements for a Digital Healthcare System to Manage Gestational Diabetes in Patients and Healthcare Professionals: A Cross-sectional Survey.

This study aimed to assess the current status of gestational diabetes mellitus (GDM) diagnosis and management, and the demand for a digital healthcare system, in order to develop an optimal digital-based management model for GDM. An anonymous online survey was conducted targeting pregnant/postpartum women (Group W), internal medicine physicians (Group P), and obstetricians (group O) from September 6, 2022 to December 31, 2022. The survey assessed the women's knowledge of GDM and gathered information about healthcare professionals' (HCPs) current GDM management practices. All groups were asked about their acceptance of and demands for a digital healthcare system for GDM. Statistical comparisons between groups were conducted using the chi-square test or Fisher's exact test where appropriate. A total of 168 participants were in Group W, 185 in Group P, and 256 in Group O. Participants from all groups recognized the need for a digital healthcare system for GDM (Group W: 95.8%, Group P: 85.9%, Group O: 60%). However, HCPs showed less willingness to integrate these systems into their clinics than pregnant/postpartum women. Essential features identified were recording blood glucose levels and insulin, along with automatic data linkage from self-monitoring devices. Group W showed a higher preference for lab test access, search functionality, and fetal weight assessment than groups P and O (all P < .0001), while Groups P and O had a greater preference for recording insulin and maternal body weight compared to Group W (P = .0141 and .0023, respectively). Both pregnant/postpartum women and HCPs acknowledged the benefits of utilizing a digital healthcare system for managing GDM. However, there were differences in perspectives among these groups.

Total Water–Soluble Flavonoids From Lithocarpus litseifolius (Hance) Chun (Sweet Tea) Improve Glucose Homeostasis Through Multitarget Signalling in GDM Mice

Background: The oral safety of Lithocarpus litseifolius (Hance) Chun (sweet tea) that has antihyperglycemic potential has been verified. However, its specific application and action mechanism in the treatment of gestational diabetes mellitus (GDM) are still unclear.Methods: Total water–soluble flavonoids extracted from L. litseifolius (Hance) Chun (sweet tea) were applied to GDM mice. The glucose tolerance, insulin sensitivity, and histopathology of the GDM mice were evaluated through an intraperitoneal glucose tolerance test (IPGTT), an intraperitoneal insulin tolerance test (IPITT), and histochemistry. The possible mechanism was analysed through network pharmacology.Results: Compared with those in GDM model mice (MD group), blood glucose levels indicating both glucose tolerance and insulin sensitivity were improved in GDM mice treated with total water–soluble flavonoids (LLHC group) but were greater than those in normal control mice (NC group). The number of apoptotic liver cells was significantly lower in the LLHC group than in the MD group, but greater than that in the NC group. Multiple targets and signalling pathways that were acted by eight main active ingredients were involved in the process by which total water–soluble flavonoids protect against GDM. The main mechanism involved quercetin (10 targets) and luteolin (8 targets), which acted on the effector target of GAA through six main signalling pathways around the AKT1 core axis.Conclusion: Oral administration of total water–soluble flavonoids can alleviate glucose intolerance and insulin resistance via the inhibition of liver cell apoptosis. The main active ingredients act on GAA through the signalling pathways of the AKT1 core axis.

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.