The Associations Between Health-Related Knowledge and Health Beliefs Regarding Risk for Gestational Diabetes in American Indian and Alaska Native Female Adolescents and Young Adults at Risk for Gestational Diabetes and Their Female Caregivers: A Cross-Sectional Dyadic Analysis.

INTRODUCTION: Research on associations between knowledge and health beliefs for women at risk for gestational diabetes mellitus (GDM) has focused on adults at risk for or having GDM. Gaps also exist in examining interpersonal associations with family members or peers. We examined dyadic associations between knowledge and health beliefs about the risk for GDM between and within American Indian and Alaska Native (AIAN) female adolescents and young adults (FAYAs) at risk for GDM and their mothers or adult female caregivers (FCs). METHODS: Grounded in the Expanded Health Belief Model, we employed a cross-sectional design using baseline data from 147 dyads of AIAN FAYAs at risk for GDM and their FCs who participated in the Stopping GDM in Daughters and Mothers trial. FAYAs were 12.0 to 24.5 years of age, and 89.1% were students. FCs had a mean (SD) age of 44.0 (9.3) years, 87.0% were AIAN, 44.9% were college educated, 19.7% had ever had GDM, and 81.0% were the FAYA's mother. FAYAs and FCs completed surveys about knowledge and health beliefs (benefits, barriers, severity, susceptibility) regarding GDM risk and prevention. Bivariate correlational analyses were performed to examine associations between and within dyad members. Dyadic associations were investigated using actor-partner interdependence modeling (APIM) assuming distinguishable dyad members. RESULTS: Compared with their FCs, FAYAs had lower health-related knowledge and perceived benefits of GDM prevention and susceptibility regarding GDM risk. APIM revealed actor and partner effects of health-related knowledge on health beliefs for dyads. In particular, positive actor effects were found for FAYAs and FCs for GDM-related knowledge with perceived benefits (P < .001), and positive partner effects of GDM-related knowledge for FCs were related to perceived susceptibility and severity for FAYAs (P < .05). DISCUSSION: As shown in these AIAN dyads, FAYAs and their FCs, as members of one another's social network, may influence each other's health beliefs regarding GDM risk and prevention.

Intrapartum maternal glycaemic control for the prevention of neonatal hypoglycaemia: a systematic review and meta-analysis.

BACKGROUND: Neonatal hypoglycaemia is the most common metabolic disorder in infants, and may be influenced by maternal glycaemic control. This systematic review evaluated the effect of intrapartum maternal glycaemic control on neonatal hypoglycaemia. METHODS: We included randomised controlled trials (RCTs), quasi-RCTs, non-randomised studies of interventions, and cohort or case-control studies that examined interventions affecting intrapartum maternal glycaemic control compared to no or less stringent control. We searched four databases and three trial registries to November 2023. Quality assessments used Cochrane Risk of Bias 1 or the Effective Public Health Practice Project Quality Assessment Tool. Certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE). Meta-analysis was performed using random-effects models analysed separately for women with or without diabetes. The review was registered prospectively on PROSPERO (CRD42022364876). RESULTS: We included 46 studies of women with diabetes and five studies of women without diabetes: one RCT, 32 cohort and 18 case-control studies (11,273 participants). For women with diabetes, the RCT showed little to no difference in the incidence of neonatal hypoglycaemia between tight versus less tight intrapartum glycaemic control groups (76 infants, RR 1.00 (0.45, 2.24), p = 1.00, low certainty evidence). However, 11 cohort studies showed tight intrapartum glycaemic control may reduce neonatal hypoglycaemia (6,152 infants, OR 0.44 (0.31, 0.63), p < 0.00001, I2 = 58%, very low certainty evidence). For women without diabetes, there was insufficient evidence to determine the effect of tight intrapartum glycaemic control on neonatal hypoglycaemia. CONCLUSIONS: Very uncertain evidence suggests that tight intrapartum glycaemic control may reduce neonatal hypoglycaemia in infants of women with diabetes. High-quality RCTs are required.

Fetal abdominal obesity in women with one value abnormality on diagnostic test for gestational diabetes mellitus.

Previous studies have shown that fetal abdominal obesity (FAO) was already observed at the time of gestational diabetes mellitus (GDM) diagnosis and persisted until delivery despite management in older and/or obese women. In this study, we investigated whether fetuses of women with milder hyperglycemia than GDM have accelerated abdominal growth, leading to adverse pregnancy outcomes. We retrospectively reviewed the medical records of 7,569 singleton pregnant women who were universally screened using a 50-g glucose challenge test (GCT) and underwent a 3-h 100-g oral glucose tolerance test (OGTT) if GCT result was ≥140mg/dL. GDM, one value abnormality (OVA), and normal glucose tolerance (NGT, NGT1: GCT negative, NGT2: GCT positive & OGTT negative) were diagnosed using Carpenter-Coustan criteria. With fetal biometry data measured simultaneously with 50-g GCT, relative fetal abdominal overgrowth was investigated by assessing the fetal abdominal overgrowth ratios (FAORs) of the ultrasonographically estimated gestational age (GA) of abdominal circumference(AC) per actual GA by the last menstruation period(LMP), biparietal diameter(BPD) or femur length(FL), respectively. FAO was defined as FAOR ≥90th percentile The FAORs of GA-AC/GA-LMP and GA-AC/GA-BPD were significantly higher in OVA subjects compared to NGT subjects but not in NGT2 subjects. Although the frequency of FAO in OVA (12.1%) was between that of NGT (9.6%) and GDM (18.3%) without statistically significant difference, the prevalence of large for gestational age at birth and primary cesarean delivery rates were significantly higher in OVA (9.8% and 29.7%) than in NGT (5.1% and 21.5%, p<0.05). Particularly, among OVA subjects with FAO, the prevalence (33.3% and 66.7%) was significantly higher than in those without FAO (9.7% and 24.2%, p<0.05). The degree of fetal abdominal growth acceleration in OVA subjects was intermediate between that of NGT and GDM subjects. OVA subjects with FAO at the time of GDM diagnosis were strongly associated with adverse pregnancy outcomes.

Effect of interpregnancy interval on the risk of gestational diabetes mellitus during a second pregnancy.

BACKGROUND: Interpregnancy interval (IPI) is associated with the risk of GDM in a second pregnancy. However, an optimal IPI is still need to be determined based on the characteristics of the population. This study aimed to analyze the effect of interpregnancy interval (IPI) on the risk of gestational diabetes mellitus (GDM) in the Chinese population. METHODS: We conducted a retrospective cohort study on female participants who had consecutive deliveries at Peking University Shenzhen Hospital from 2013 to 2021. The IPI was categorized into 7 groups and included into the multivariate logistic regression model with other confound factors. Analysis was also stratified based on age of first pregnancy, BMI, and history of GDM. Adjusted OR values (aOR) and 95% confidence intervals (CI) calculated. The regression coefficient of IPI months on GDM prediction risk was analyzed using a linear regression model. RESULTS: A total of 2,392 participants were enrolled. The IPI of the GDM group was significantly greater than that of the non-GDM group (P < 0.05). Compared with the 18-24 months IPI category, participants with longer IPIs (24-36 months, 36-48 months, 48-60 months, and ≥ 60 months) had a higher risk of GDM (aOR:1.585, 2.381, 2.488, and 2.565; 95% CI: 1.021-2.462, 1.489-3.809, 1.441-4.298, and 1.294-5.087, respectively). For participants aged < 30 years or ≥ 30 years or without GDM history, all longer IPIs (≥ 36 months) were all significantly associated with the GDM risk in the second pregnancy (P < 0.05), while any shorter IPIs (< 18 months) was not significantly associated with GDM risk (P > 0.05). For participants with GDM history, IPI 12-18 months, 24-36 months, 36-48 months, and ≥ 60 months were all significantly associated with the GDM risk (aOR: 2.619, 3.747, 4.356, and 5.373; 95% CI: 1.074-6.386, 1.652-8.499, 1.724-11.005, and 1.078-26.793, respectively), and the slope value of linear regression (0.5161) was significantly higher compared to participants without a history of GDM (0.1891) (F = 284.168, P < 0.001). CONCLUSIONS: Long IPI increases the risk of GDM in a second pregnancy, but this risk is independent of maternal age. The risk of developing GDM in a second pregnancy for women with GDM history is more significantly affected by IPI.

The associations of maternal liver biomarkers in early pregnancy with the risk of gestational diabetes mellitus: a prospective cohort study and Mendelian randomization analysis.

Background: Associations of liver function with the risk of gestational diabetes mellitus (GDM) remain unclear. This study aimed to examine the relationship and the potential causality between maternal liver biomarkers and the risk of subsequent GDM, as well as to evaluate the interaction between liver biomarkers and lipids on GDM risk. Methods: In an ongoing Zhoushan Pregnant Women Cohort, pregnant women who finished the first prenatal follow-up record, underwent liver function tests in early pregnancy, and completed the GDM screening were included in this study. Logistic regression models were used to investigate the association, and the inverse-variance weighted method supplemented with other methods of two-sample Mendelian randomization (MR) analysis was applied to deduce the causality. Results: Among 9,148 pregnant women, 1,668 (18.2%) developed GDM. In general, the highest quartile of liver function index (LFI), including ALT, AST, GGT, ALP, and hepatic steatosis index, was significantly associated with an increased risk of GDM (OR ranging from 1.29 to 3.15), especially an elevated risk of abnormal postprandial blood glucose level. Moreover, the causal link between ALT and GDM was confirmed by the MR analysis (OR=1.28, 95%CI:1.05-1.54). A significant interaction between AST/ALT and TG on GDM risk was observed (P interaction = 0.026). Conclusion: Elevated levels of LFI in early pregnancy were remarkably associated with an increased risk of GDM in our prospective cohort. Besides, a positive causal link between ALT and GDM was suggested.

Correlation between newborn weight and serum BCAAs in pregnant women with diabetes.

BACKGROUND: Branched-chain amino acids (BCAAs), including leucine, isoleucine, and valine, are essential amino acids for mammals. Maternal BCAAs during pregnancy have been associated with newborn development. Meanwhile, BCAAs have been tightly linked with insulin resistance and diabetes in recent years. Diabetes in pregnancy is a common metabolic disorder. The current study aims to assess the circulating BCAA levels in pregnant women with diabetes and their relationship with neonatal development. METHODS: The serum concentrations of BCAAs and their corresponding branched-chain α-keto acids (BCKAs) catabolites in 33 pregnant women with normal glucose tolerance, 16 pregnant women with type 2 diabetes before pregnancy (PDGM), and 15 pregnant women with gestational diabetes mellitus (GDM) were determined using a liquid chromatography system coupled to a mass spectrometer. The data were tested for normal distribution and homogeneity of variance before statistical analysis. Correlations were computed with the Pearson correlation coefficient. RESULTS: The maternal serum BCAAs and BCKAs levels during late pregnancy were higher in women with PGDM than those in healthy women. Meanwhile, the circulating BCAAs and BCKAs showed no significant changes in women with GDM compared with those in healthy pregnant women. Furthermore, the circulating BCAA and BCKA levels in women with PGDM were positively correlated with the weight of the newborn. The circulating leucine level in women with GDM was positively correlated with the weight of the newborn. BCAA and BCKA levels in healthy pregnant women showed no correlation with newborn weight. CONCLUSIONS: The serum BCAAs in pregnant women with diabetes, which was elevated in PGDM but not GDM, were positively correlated with newborn weight. These findings highlight potential approaches for early identification of high-risk individuals and interventions to reduce the risk of adverse pregnancy outcomes.

Real-time predictive model of extrauterine growth retardation in preterm infants with gestational age less than 32 weeks.

The aim of this study was to develop a real-time risk prediction model for extrauterine growth retardation (EUGR). A total of 2514 very preterm infants were allocated into a training set and an external validation set. The most appropriate independent variables were screened using univariate analysis and Lasso regression with tenfold cross-validation, while the prediction model was designed using binary multivariate logistic regression. A visualization of the risk variables was created using a nomogram, while the calibration plot and receiver operating characteristic (ROC) curves were used to calibrate the prediction model. Clinical efficacy was assessed using the decision curve analysis (DCA) curves. Eight optimal predictors that namely birth weight, small for gestation age (SGA), hypertensive disease complicating pregnancy (HDCP), gestational diabetes mellitus (GDM), multiple births, cumulative duration of fasting, growth velocity and postnatal corticosteroids were introduced into the logistic regression equation to construct the EUGR prediction model. The area under the ROC curve of the training set and the external verification set was 83.1% and 84.6%, respectively. The calibration curve indicate that the model fits well. The DCA curve shows that the risk threshold for clinical application is 0-95% in both set. Introducing Birth weight, SGA, HDCP, GDM, Multiple births, Cumulative duration of fasting, Growth velocity and Postnatal corticosteroids into the nomogram increased its usefulness for predicting EUGR risk in very preterm infants.

Non-Targeted Metabolomics Analysis of Mother and Infant in Gestational Diabetes Mellitus and Neonatal Clinical Characterization.

BACKGROUND: The goal was to analyze serums of GDM patients and healthy pregnant women using HPLC-MS and preliminarily screen differential metabolites by metabolomics. METHOD: Sixty pregnant women who underwent elective cesarean section at term in Dongguan Dalang Hospital from January 2023 to April 2023 were selected and divided into the GDM group and healthy pregnancy group. Pre-pregnancy and pregnancy examination information, such as age, BMI, OGTT results, triglyceride, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and other clinical data were col-lected for statistical analysis. Non-targeted metabolomics of serum from 30 GDM patients and 30 healthy pregnant women were studied by HPLC-MS, and different ions were searched. The structures of differential metabolites were identified by HMDB database. The metabolic pathways of differential metabolites were analyzed by KEGG database. RESULTS: The OGTT result, pCO2, pO2, HCO3, BE, Apgar score, and bilirubin levels in the GDM group were higher than those in the healthy pregnancy group (p < 0.05). However, there were no significant differences in age, triglyceride, total cholesterol, newborn birth weight, newborn birth blood glucose, and blood gas pH between the two groups (all p > 0.05). Using p < 0.05 as the screening standard, 55 differential metabolites were identified in serum, mainly including fatty acyl, carboxylic acids and their derivatives, steroids and their derivatives, ketoacids and their derivatives, and pyrimidine nucleosides, etc., all of which were up-regulated or down-regulated to varying degrees. The 55 metabolites were mainly involved in the metabolism of pyrimidine, pyruvate, alanine, aspartic acid, glutamic acid, and arachidonic acid, glycolysis, and biosynthesis of unsaturated fatty acids. CONCLUSIONS: The discovery of these metabolites provides a theoretical basis for an indepth understanding of GDM pathogenesis. Non-targeted metabonomics analysis of blood metabonomics research technology has shown great potential value in the early diagnosis of obstetric diseases and the study of disease mechanisms.

Design, rationale and protocol for Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs): an observational cohort study.

INTRODUCTION: Given the increasing prevalence of both obesity and pre-diabetes in pregnant adults, there is growing interest in identifying hyperglycaemia in early pregnancy to optimise maternal and perinatal outcomes. Multiple organisations recommend first-trimester diabetes screening for individuals with risk factors; however, the benefits and drawbacks of detecting glucose abnormalities more mild than overt diabetes in early gestation and the best screening method to detect such abnormalities remain unclear. METHODS AND ANALYSIS: The goal of the Glycemic Observation and Metabolic Outcomes in Mothers and Offspring study (GO MOMs) is to evaluate how early pregnancy glycaemia, measured using continuous glucose monitoring and oral glucose tolerance testing, relates to the diagnosis of gestational diabetes (GDM) at 24-28 weeks' gestation (maternal primary outcome) and large-for-gestational-age birth weight (newborn primary outcome). Secondary objectives include relating early pregnancy glycaemia to other adverse pregnancy outcomes and comprehensively detailing longitudinal changes in glucose over the course of pregnancy. GO MOMs enrolment began in April 2021 and will continue for 3.5 years with a target sample size of 2150 participants. ETHICS AND DISSEMINATION: GO MOMs is centrally overseen by Vanderbilt University's Institutional Review Board and an Observational Study Monitoring Board appointed by National Institute of Diabetes and Digestive and Kidney Diseases. GO MOMs has potential to yield data that will improve understanding of hyperglycaemia in pregnancy, elucidate better approaches for early pregnancy GDM screening, and inform future clinical trials of early GDM treatment. TRIAL REGISTRATION NUMBER: NCT04860336.

[Development and Effectiveness Evaluation of Comprehensive Mobile-Based, Breastfeeding Promotion Program for Mothers with Gestational Diabetes].

PURPOSE: This study aimed to evaluate the effects of a mobile-based breastfeeding promotion program (M-BFGDM) that helps mothers with gestational diabetes. METHODS: Forty-seven mothers participated in the study, of whom 22 were in the experimental group and 25 in the control group. To verify the effects, a lag design before and after the non-equivalence control group was used. The data collection for the experimental group was done before and after the intervention. RESULTS: In the results, breastfeeding knowledge showed a significant difference in the interaction between measurement period and group (χ² = 8.14, p = .017), whereas breastfeeding intention did not show a significant difference in the interaction (χ² = 4.73, p = .094). There was no difference in self-efficacy interaction (F = 0.13, p = .856). The breastfeeding method showed no difference in interaction (F = 0.04, p = .952), whereas cross-analysis showed a significant difference in breastfeeding practice rate between the experimental group and the control group at 1 month postpartum (χ² = 7.59, p = .006). CONCLUSION: A mobile-based breastfeeding promotion program was developed and applied for gestational diabetic mothers, resulting in an increase in breastfeeding knowledge and an improvement in breastfeeding practice rate one month after childbirth. In addition, M-BFGDM managed to create a breastfeeding practice environment with fewer time and place restrictions. A program study that complements motivation is needed to improve breastfeeding in pregnant diabetic mothers in the future.

Validation of the Gestational Diabetes Mellitus Knowledge Questionnaire (GDMKQ) among Filipino Patients in a Tertiary Medical Center.

Objectives: Gestational diabetes mellitus (GDM) is a common pregnancy complication with adverse fetal and maternal outcomes. Currently, there are only a few validated tools available that address knowledge in GDM. Recognition of the inconsistencies will provide an effective learning program to achieve optimal results. This study aimed at validating the "Gestational Diabetes Mellitus Knowledge Questionnaire" (GDMKQ). Methodology: A cross-sectional validation study on GDMKQ among 51 GDM patients aged at least 18 years was conducted in the outpatient clinics of a tertiary hospital. Excluded were those with pre-existing diabetes. The questionnaire was submitted for peer review for translation to Filipino and back-translation. Concurrent validity, internal consistency and test-retest reliability of the questionnaire were undertaken as part of the validation process. Descriptive analysis was used for data elaboration by using SPSS v23. Results: The Filipino version of GDMKQ demonstrated sensible content and face validity. As measured, respondents obtained higher total and domain scores with better knowledge levels of GDM compared to its English version. Overall adequate knowledge was observed among those married and college subgroups as compared to single women and those with secondary levels of education. The reliability of the questionnaire was calculated at 0.632 using the Kuder-Richardson 20. The test-retest scores using the Filipino-translated questionnaire have a Pearson correlation coefficient of 0.853 with moderate to good level of agreement with each other, and Cohen's kappa of 0.564 with an intra-class correlation coefficient of 0.828. Conclusion: The Filipino-translated version of GDMKQ is a valid screening tool that assesses a patient's knowledge on gestational diabetes. Identifying the level of their understanding will enable clinicians to develop an individualized, effective learning program to improve pregnancy outcomes.

Gestational diabetes mellitus: relationship of adverse outcomes with severity of disease.

AIMS: To derive accurate estimates of risk of maternal and neonatal complications in women with gestational diabetes mellitus (GDM) and to investigate the association of the effect size of these risks on subgroups of GDM managed with dietary modification, metformin and insulin therapy. METHODS: This was a large retrospective cohort study undertaken at a large maternity unit in the United Kingdom between January 2010 and June 2022. We included singleton pregnancies that booked at our unit at 11-13 weeks' gestation. The rates of maternal and neonatal complications in pregnancies with GDM that were managed by a multidisciplinary team (MDT) in the specialist high-risk clinic were compared to those in non-diabetic pregnancies. We stratified pregnancies with GDM into those that were managed with diet, metformin and insulin to pregnancies without diabetes. Logistic regression analysis was carried out to determine risks of pregnancy complications in pregnancies with GDM and its treatment subgroups. Risks were expressed as absolute risks (AR) and odds ratio (OR) (95% confidence intervals [CI]). Forest plots were used to graphically demonstrate risks. RESULTS: The study population included 51,211 singleton pregnancies including 2089 (4.1%) with GDM and 49,122 (95.9%) controls without diabetes. In pregnancies with GDM, there were 1247 (59.7%) pregnancies managed with diet, 451 (21.6%) with metformin and 391 (18.7%) who required insulin for maintaining euglycaemia. Pregnancies with GDM had higher maternal age, body mass index (BMI), higher rates of Afro-Caribbean and South Asian racial origin and higher rates of chronic hypertension. In pregnancies with GDM compared to non-diabetic controls, there was an increased rate of preterm delivery, delivery of LGA neonate, polyhydramnios, preeclampsia, need for IOL, elective and emergency CS and PPH whereas the rate of delivery of SGA neonates and likelihood of an unassisted vaginal delivery were lower. In pregnancies with GDM, there is significantly increased risk of maternal and neonatal complications in those that require insulin compared to those that are managed on dietary modification alone. CONCLUSIONS: There is a linear association between the risk of adverse outcomes and the severity of GDM with those on insulin treatment demonstrating an increased association with complications compared to those that have milder disease requiring only dietary modification.

A comparison of the breast milk microbiota from women diagnosed with gestational diabetes mellitus and women without gestational diabetes mellitus.

BACKGROUND: Human breast milk (HBM) is a contributing factor in modulating the infant's gut microbiota, as it contains bacteria that are directly transferred to the infant during breastfeeding. It has been shown that children of women diagnosed with gestational diabetes mellitus (GDM) have a different gut microbiota compared to children of women without GDM. Our hypothesis is therefore that women with GDM have a different HBM microbiota, which may influence the metabolic function and capacity of the child later in life. The aim of this study was to investigate whether women with GDM have a different breast milk microbiota 1-3 weeks postpartum compared to women without GDM. METHODS: In this case-control study, a total of 45 women were included: 18 women with GDM and 27 women without GDM. A milk sample was collected from each participant 1 to 3 weeks postpartum and the bacterial composition was examined by 16 S rRNA gene sequencing targeting the V4 region. RESULTS: High relative abundances of Streptococcus and Staphylococcus were present in samples from both women with and without GDM. No difference could be seen in either alpha diversity, beta diversity, or specific taxa between groups. CONCLUSION: Our results did not support the existence of a GDM-associated breast milk microbiota at 1-3 weeks postpartum. Further research is needed to fully understand the development of the gut microbiota of infants born to mothers with GDM.

Association between maternal blood lipids and neonatal hypoglycaemia in pregnancy with gestational diabetes mellitus: a cohort study.

BACKGROUND: Gestational diabetes mellitus (GDM) prevalence is on the rise globally. Offspring of diabetic mothers face increased risk of neonatal hypoglycaemia (NH), and women with GDM have abnormal lipid profiles. However, there is no consensus on the link between maternal blood lipids and NH in infants from mothers with GDM. This study aimed to explore how maternal blood lipids affect NH. METHODS: A retrospective cohort study was conducted at the First Affiliated Hospital of Sun Yat-sen University. Information on participants' baseline characteristics and maternal metabolic profiles of glucose and lipids was collected. Significant variables from the univariate analysis were included in logistic regression, which was used to construct the predictive model for NH. A nomogram was constructed for visualizing the model and assessed using the area under the receiver operating characteristic (ROC) curve (AUC). RESULTS: Neonatal capillary blood glucose (CBG) decreased rapidly in the first hour after birth, increased gradually from the first to the second hour, and then remained stable. In the NH group, 86.11% (502/583) of hypoglycaemia cases occurred within the first two hours after birth. Multivariate logistic regression suggested that the lipid indices of maternal apoprotein B/apoprotein A1 (Apo-B/Apo-A1) (odds ratio (OR) = 1.36, 95% confidence intervals (CIs): 1.049-1.764, P = 0.02) and apoprotein E (Apo-E) (OR = 1.014, 95% CIs: 1.004-1.024, P = 0.004) were positively associated with NH in neonates from mothers with GDM. Triglycerides (TGs) (OR = 0.883, 95% CIs: 0.788-0.986, P = 0.028) were inversely associated with NH. Maternal glycated haemoglobin (HbA1c), age, twin pregnancy and caesarean delivery also had predictive value of NH. The AUC of the nomogram derived from these factors for the prediction model of NH was 0.657 (95% CIs: 0.630-0.684). CONCLUSIONS: The present study revealed that the Apo-B/Apo-A1 and Apo-E levels were associated with an increased risk of NH. A nomogram was developed to forecast the risk of NH in babies born to mothers with GDM, incorporating maternal blood lipids, HbA1c, age, twin pregnancy, and caesarean section. The trajectory of glycaemia for neonates indicates the need for intensive CBG monitoring within 2 h of birth for neonates from mothers with GDM.

Relationship between gut microbiota and the pathogenesis of gestational diabetes mellitus: a systematic review.

Introduction: Gestational diabetes mellitus (GDM) is a form of gestational diabetes mellitus characterized by insulin resistance and abnormal function of pancreatic beta cells. In recent years, genomic association studies have revealed risk and susceptibility genes associated with genetic susceptibility to GDM. However, genetic predisposition cannot explain the rising global incidence of GDM, which may be related to the increased influence of environmental factors, especially the gut microbiome. Studies have shown that gut microbiota is closely related to the occurrence and development of GDM. This paper reviews the relationship between gut microbiota and the pathological mechanism of GDM, in order to better understand the role of gut microbiota in GDM, and to provide a theoretical basis for clinical application of gut microbiota in the treatment of related diseases. Methods: The current research results on the interaction between GDM and gut microbiota were collected and analyzed through literature review. Keywords such as "GDM", "gut microbiota" and "insulin resistance" were used for literature search, and the methodology, findings and potential impact on the pathophysiology of GDM were systematically evaluated. Results: It was found that the composition and diversity of gut microbiota were significantly associated with the occurrence and development of GDM. Specifically, the abundance of certain gut bacteria is associated with an increased risk of GDM, while other changes in the microbiome may be associated with improved insulin sensitivity. In addition, alterations in the gut microbiota may affect blood glucose control through a variety of mechanisms, including the production of short-chain fatty acids, activation of inflammatory pathways, and metabolism of the B vitamin group. Discussion: The results of this paper highlight the importance of gut microbiota in the pathogenesis of GDM. The regulation of the gut microbiota may provide new directions for the treatment of GDM, including improving insulin sensitivity and blood sugar control through the use of probiotics and prebiotics. However, more research is needed to confirm the generality and exact mechanisms of these findings and to explore potential clinical applications of the gut microbiota in the management of gestational diabetes. In addition, future studies should consider the interaction between environmental and genetic factors and how together they affect the risk of GDM.

Detection of Hanseniaspora opuntiae in anovaginal samples of pregnant women in Rio de Janeiro, Brazil-a case report.

In this study, we report the first isolation of Hanseniaspora opuntiae obtained from four pregnant women in Brazil. Clinical isolates were obtained from four samples taken between 35 and 37 gestational weeks, as part of the routine antenatal care for maternal colonization screening for Streptococcus agalactiae group B. The patients were immunocompetent, with two of them diagnosed with gestational diabetes mellitus. Species identification was performed by MALDI-TOF MS and rDNA sequencing. While Hanseniaspora species have not traditionally been considered a typical opportunist pathogen, our findings emphasize the importance of investigating and screening for Hanseniaspora in pregnant populations, highlighting H. opuntiae as a potential agent of human infections.

Investigation of the relationship between air pollution and gestational diabetes.

BACKGROUND: Gestational diabetes mellitus (GDM) can have negative effects on both the pregnancy and perinatal outcomes, as well as the long-term health of the mother and the child. It has been suggested that exposure to air pollution may increase the risk of developing GDM. This study investigated the relationship between exposure to air pollutants with gestational diabetes. METHODS: The present study is a retrospective cohort study. We used data from a randomised community trial conducted between September 2016 and January 2019 in Iran. During this period, data on air pollutant levels of five cities investigated in the original study, including 6090 pregnant women, were available. Concentrations of ozone (O3), nitric oxide (NO), nitrogen dioxide (NO2), nitrogen oxides (NOx), sulphur dioxide (SO2), carbon monoxide (CO), particulate matter < 2.5 (PM2.5) or <10 µm (PM10) were obtained from air pollution monitoring stations. Exposure to air pollutants during the three months preceding pregnancy and the first, second and third trimesters of pregnancy for each participant was estimated. The odds ratio was calculated based on logistic regression in three adjusted models considering different confounders. Only results that had a p < .05 were considered statistically significant. RESULTS: None of the logistic regression models showed any statistically significant relationship between the exposure to any of the pollutants and GDM at different time points (before pregnancy, in the first, second and third trimesters of pregnancy and 12 months in total) (p > .05). Also, none of the adjusted logistic regression models showed any significant association between PM10 exposure and GDM risk at all different time points after adjusting for various confounders (p > .05). CONCLUSIONS: This study found no association between GDM risk and exposure to various air pollutants before and during the different trimesters of pregnancy. This result should be interpreted cautiously due to the lack of considering all of the potential confounders.

The health of pregnant women and their children can be impacted by gestational diabetes mellitus (GDM), one of the prevalent pregnancy complications. Some of studies showed that the incidence of gestational diabetes can be influenced by genetic or environmental factors. Air pollution is an environmental stimulus that may predispose pregnant women to GDM. This research explored whether air pollution could increase the risk of developing gestational diabetes. Over 6000 pregnant women in five cities of Iran participated in the study and were screened for gestational diabetes. Their exposure to the various air pollutants during the three months preceding pregnancy and total pregnancy period was measured. In this study, we found no clear association between air pollution and gestational diabetes. However, this finding needs to be interpreted cautiously since all the influential factors were not assessed.

Association between maternal and fetal inflammatory biomarkers and offspring weight and BMI during the first year of life in pregnancies with GDM: MySweetheart study.

Background: Gestational Diabetes Mellitus (GDM) is frequently associated with chronic, low-grade inflammation. Whether this environment affects offspring anthropometry during early childhood remains to be elucidated. The aim of this study was to investigate the associations between maternal and fetal (cord blood-umbilical artery) inflammatory biomarkers and offspring weight and BMI up to 1 year in pregnancies with GDM. Methods: In this prospective secondary analysis of the MySweetheart study, we included 193 women with GDM and their offspring. Maternal and fetal (N=39) predictors included serum levels of inflammatory biomarkers including CRP, IL-6, and TNF-α at 24-32 weeks of gestational age (GA) and in the cord blood. Offspring outcomes were small and large for gestational age (SGA, LGA), sex- and age-adjusted weight, and BMI at birth and at 1 year. Univariate and multivariate regression models were performed. Associations were adjusted for maternal pre-pregnancy BMI, age, and ethnicity. Results: Mean maternal age was 33.6 ± 4.8 years, and pre-pregnancy BMI 25.9 ± 5.6 kg/m2. Their mean gestational age at the 1st GDM visit was 29 ± 2.4 weeks. Gestational age at delivery was 39.7 ± 1.1 weeks, with a mean birthweight of 3.4 ± 0.46 kg; 11.8% of offspring were LGA and 10.8% were SGA. At 1 year of age, mean offspring weight was 9.8 ± 1.2 kg and BMI z-score 0.23 ± 1.1 kg/m2. In the models including only maternal predictors, TNF-α at 24-32 weeks of GA was positively associated with SGA and inversely with offspring weight and BMI at birth and at 1 year (p ≤0.034). In the models including only fetal predictors and the combined model, CRP was inversely associated with BMI at 1 year (p ≤0.020). Conclusions: In women with GDM, maternal and fetal inflammatory biomarkers distinctively influenced offspring anthropometry during the first year of life, independent of maternal age, prepregnancy BMI and ethnicity. These results suggest that low-grade inflammation during pregnancy may affect the developing offspring by leading to a decrease in weight and BMI and may have implications for future personalized follow-up of women with GDM and their offspring.

Ultrasonographic placental parameters at 11-13+6 weeks' gestation in the prediction of complications in pregnancy after assisted reproductive technology.

OBJECTIVE: To evaluate the performance of maternal factors, biophysical and biochemical markers at 11-13 + 6 weeks' gestation in the prediction of gestational diabetes mellitus with or without large for gestational age (GDM ± LGA) fetus and great obstetrical syndromes (GOS) among singleton pregnancy following in-vitro fertilisation (IVF)/embryo transfer (ET). MATERIALS AND METHODS: A prospective cohort study was conducted between December 2017 and January 2020 including patients who underwent IVF/ET. Maternal mean arterial pressure (MAP), ultrasound markers including placental volume, vascularisation index (VI), flow index (FI) and vascularisation flow index (VFI), mean uterine artery pulsatility index (mUtPI) and biochemical markers including placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) were measured at 11-13 + 6 weeks' gestation. Logistic regression analysis was performed to determine the significant predictors of complications. RESULTS: Among 123 included pregnancies, 38 (30.9%) had GDM ± LGA fetus and 28 (22.8%) had GOS. The median maternal height and body mass index were significantly higher in women with GDM ± LGA fetus. Multivariate logistic regression analysis demonstrated that in the prediction of GDM ± LGA fetus and GOS, there were significant independent contributions from FI MoM (area under curve (AUROC) of 0.610, 95% CI 0.492-0.727; p = 0.062) and MAP MoM (AUROC of 0.645, 95% CI 0.510-0.779; p = 0.026), respectively. CONCLUSION: FI and MAP are independent predictors for GDM ± LGA fetus and GOS, respectively. However, they have low predictive value. There is a need to identify more specific novel biomarkers in differentiating IVF/ET pregnancies that are at a higher risk of developing complications.

Adverse pregnancy outcomes in gestational diabetes mellitus: a systematic review and meta-analysis protocol.

INTRODUCTION: Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes, including adverse outcomes for both the mother and the fetus. Different diagnostic criteria are used for GDM, and it is not clear how these affect the reported prevalence of adverse pregnancy outcomes. This protocol is for a systematic review to describe and compare the prevalence of adverse pregnancy outcomes in GDM using the different diagnostic criteria applied in various countries/regions of the world. METHODS AND ANALYSIS: A systematic review and meta-analysis will be carried out. A comprehensive search of observational studies that report the outcomes of interest to this review from 2010 to 2021 will be conducted. We will search the major electronic databases such as PubMed, Scopus, CINHAL and Google Scholar, and screen references of included studies for additional studies. Meta-analyses will be performed, if there is low heterogeneity, and pooled estimates per outcome reported. We will use the bias-adjusted inverse variance heterogeneity model and random effects models, depending on the heterogeneity observed, to pool prevalence estimates and perform subgroup analyses by region, by age group, by diagnostic criteria and by GDM screening method if sufficient data are available. We will also compare the prevalence of adverse outcomes by diagnostic method and report prevalence ratios. We will report 95% confidence estimates for all estimates. ETHICS AND DISSEMINATION: Ethical approval is not required as the review uses published data. Findings will be published in peer-reviewed journals and presented at conferences. PROSPERO REGISTRATION NUMBER: CRD42020155061.