The human milk endocannabinoidome and neonatal growth in gestational diabetes.

Objective: Endocannabinoids and their N-acyl-ethanolamines (NAEs) and 2monoacyl-glycerols (2-MAGs) congeners are involved in the central and peripheral regulation of energy homeostasis, they are present in human milk and are associated with obesity. Infants exposed in utero to gestational diabetes mellitus (GDM) are more likely to develop obesity. The objective of this cross-sectional study is to compare the profile of eCBome mediators in milk of women with gestational diabetes (GDM+) and without (GDM-) and to assess the association with offspring growth. The hypothesis is that the eCBome of GDM+ human milk is altered and associated with a difference in infant growth. Methods: Circulating eCBome mediators were measured by LC-MS/MS in human milk obtained at 2 months postpartum from GDM+ (n=24) and GDM- (n=29) women. Infant weight and height at 2 months were obtained from the child health record. Z-scores were calculated. Results: Circulating Npalmitoylethanolamine (PEA) was higher in human milk of GDM+ women than in GDM- women (4.9 ± 3.2 vs. 3.3 ± 1.7, p=0.04). Higher levels were also found for several 2monoacyl-glycerols (2-MAGs) (p<0.05). The levels of NAEs (ß=-4.6, p=0.04) and especially non-omega-3 NAEs (B=-5.6, p=0.004) in human milk were negatively correlated with weight-for-age z-score of GDM+ offspring. Conclusion: The profile of eCBome mediators in human milk at 2 months postpartum was different in GDM+ compared to GDM- women and was associated with GDM+ offspring growth at 2 months. Clinical trial registration: ClinicalTrials.gov, identifier (NCT04263675 and NCT02872402).

Prevalence and Risk Factors of Confirmed Gestational Diabetes Mellitus Among Pregnant Women With Prior Positive Screening: A Case-Control Study.

BACKGROUND:  Saudi Arabia has a higher rate of gestational diabetes mellitus (GDM) than most other countries. There is a paucity of data on the risk factors for GDM, particularly positive screening for diabetes in the initial period of pregnancy. OBJECTIVES:  The aim of this study was to determine the prevalence of confirmed GDM in pregnant women who initially screened positive for GDM, as well as to identify its association with age, nationality, and clinical risk factors. PATIENTS AND METHODS:  This case-control study was conducted retrospectively at a tertiary referral center in Jeddah, Saudi Arabia. It included pregnant women who were referred between January 2019 and December 2022 after having tested positive on a 50 g oral glucose tolerance test (OGTT). They subsequently underwent a 75 g or 100 g confirmatory OGTT at our center. The sociodemographic and clinical characteristics of those with confirmed GDM (cases) and those with negative confirmatory OGTT (controls) were compared. RESULTS:  The majority of participants (75.4%) had confirmed GDM. However, there were no significant differences between cases and controls with regard to age, nationality, or clinical or pregnancy-related factors. Of note, the cohort was characterized by high gravidity and high parity, which may indicate susceptibility to GDM. CONCLUSION:  The study findings support the usefulness of the 50 g OGTT for the screening of pregnant women at high risk for GDM. In addition, high gravidity and parity may also be risk factors for GDM, warranting closer monitoring for GDM and further research in a high-natality population such as that of Saudi Arabia.

Therapeutic Potential of Thunbergia laurifolia L. Extract in Gestational Diabetes Mellitus: Insights from a Rat Model.

OBJECTIVE: To assess the effects of Thunbergia laurifolia L. extract (TLE) on gestational diabetes mellitus (GDM) in a rat model. METHODS: Thunbergia laurifolin L. leaves were subjected to ethanolic extraction. In vivo study, 50 pregnant rats were randomly divided into 5 groups (10 for each): non-GDM group, GDM induced by streptozotocin (STZ, 60 mg/kg i.p.), metformin (MET) 100 mg/kg, TLE 50, and 500 mg/kg groups. Administration was performed on gestation day 7 until term (day 21). The effects of TLE on blood glucose, insulin levels, lipid profiles, liver enzymes, and maternal performances were assessed. In in vitro study, the effect of TLE was examined using the organ bath for uterine force measurement. RESULTS: In in vivo study, TLE significantly reduced blood glucose as compared to GDM (P<0.05) with gradually increased insulin level. This effect was consistent with islets of Langerhans restoration. Histologically, the uterine muscular layer displayed a marked increase in fiber area in response to both doses as compared to GDM (P<0.05). Additionally, TLE significantly reduced total cholesterol, triglyceride, and alanine transaminase levels (P<0.05). Intriguingly, TLE also led to a notable augmentation in gravid uterus size, live fetuses count, and implantation numbers, while significantly reducing the post-implantation loss rate associated with fetal classification (P<0.05). Thus, GDM improvements were close to those produced by MET. In in vitro study, TLE exerted a concentration-dependent inhibition of spontaneous uterine contractility (half-maximal inhibition concentration=1.2 mg/L). This inhibitory effect extended to potassium chloride depolarization and oxytocin-mediated contractions. When combined with its major constituent, rosmarinic acid, TLE produced an enhanced inhibitory effect (P<0.05). CONCLUSIONS: TLE ameliorated blood glucose levels, enhanced uterine muscular structure, and improved maternal and fetal performance in GDM. TLE also displayed tocolytic properties. These findings underscore the need for further exploration of TLE as a potential tocolytic agent to mitigate GDM-associated complications.

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.

The glucose tolerance peak parameter revisited. Definition for a novel use in Gestational Diabetes Mellitus confirmation.

Aims: This study aimed to establish a decisive threshold for the Glucose Tolerance peak (GTp) parameter in diagnosing Gestational Diabetes Mellitus (GDM) and to assess its diagnostic efficacy in comparison with other commonly employed indexes in clinical practice. Materials and methods: Conducted as a prospective observational cohort, the study enrolled 92 pregnant women between 24-28 weeks of gestation, who underwent an Oral glucose Tolerance Test (OGTT) 100 gr. following a positive O'Sullivan screening at La Paz University Hospital. An additional 30-min sample was incorporated to assess the insulin response dynamics during hyperglycaemia. Basal indices and those derived from the OGTT 100 gr. test were computed. Receiver Operating Characteristic (ROC) curves were utilized to determine the optimal cut-off points for the indexes derived from the OGTT. Informed written consent was obtained from all participants. Results: Significantly greater glucose tolerance, as indicated by GTp, was observed in the Non-Gestational Diabetes (NTG) pregnant group (p < 0.01). The GTp emerged as the parameter with the highest positive predictive value for GDM diagnosis. A cut-off of < 0.36 demonstrated 100% specificity and 75% sensitivity in diagnosing GDM. Conclusions: GTp, an index derived exclusively from the OGTT peak glycaemia, proves valuable in confirming the presence of GDM. The GTp could be used to confirm the presence of GDM under necessity of a second OGTT as test confirmation in pregnant woman. A cut-off of < 0. 36 has a specificity of 100% and a sensitivity of 75% for the diagnosis of GDM.

Risk prediction models for postpartum glucose intolerance in women with a history of gestational diabetes mellitus: a scoping review.

Objective: The objective of this scoping review was to investigate the effectiveness and limitations of risk prediction models for postpartum glucose intolerance in women with gestational diabetes mellitus (GDM). The aim was to provide valuable insights for healthcare professionals in the development of robust risk prediction models. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, EBSCO, Web of Science Core Collection, Ovid Full-Text Medical Journal Database, ProQuest, Elsevier ClinicalKey, China National Knowledge Infrastructure, China Biology Medicine, and WanFang Database, spanning from January 1990 to July 2023. To assess the quality of the included models, the Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed. Results: Fourteen relevant studies were identified and included in the final review, all focusing on model development. The discrimination ability of the included models ranged from 0.725 to 0.940, indicating satisfactory prediction accuracy. However, a notable limitation was that nine of these models (64.3%) did not provide clear guidelines on the selection of potential predictors. Furthermore, only six models (42.86%) underwent internal validation, with none undergoing external validation. A high risk of bias was observed across the included models. Logistic regression, Cox regression, and machine learning were the primary methods employed in the construction of these models. Conclusion: The risk prediction models included in this review demonstrated favorable prediction accuracy. However, due to variations in construction methodologies, direct comparison of their performance is challenging. These models exhibited certain shortcomings, such as inadequate handling of missing data and a lack of internal and external validation, resulting in a high risk of bias. Therefore, it is recommended that these models be updated and externally validated. The development of prospective, multi-center studies is encouraged to construct predictive models with low risk of bias and high clinical applicability, ultimately guiding evidence-based clinical practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-023-01330-1.

The dietary supplements effect on metabolic markers in non-pharmacologically managed gestational diabetes mellitus patients: a systematic review and meta-analysis and meta-regression of randomized controlled trials.

Background: Although several randomized clinical trials have tested the effect of prenatal dietary supplements on plasma glucose and lipid levels in non-pharmacologically managed gestational diabetes mellitus patients (GDM), a rigorous meta-analytic compendium lacks in the context. Therefore, this study aims to address this evidence gap. Method: Eligible trials retrieved from searches in the PubMed, Embase, and Scopus databases were appraised using the Revised Cochrane risk-of-bias tool for randomized trials (RoB 2). The weighted mean differences (WMD) between dietary supplements and placebo were estimated using random-effect meta-analysis models for plasma glycemic and lipid markers. Meta-regression analysis ensued for effect modifier identification. The statistical significance estimation happened at p < 0.05 (95% confidence interval). Results: This review included 19 trials (mostly Iranian and of low risk of bias primarily) of > 8000 GDM patients. Meta-analysis showed favorable effects of dietary supplementation on fasting plasma glucose (WMD: -5.42 mg/dL, p < 0.001), homeostasis model assessment indexes- insulin resistance (HOMA-IR; WMD: -1.02, p < 0.001), quantitative insulin sensitivity check index (WMD: 0.01, p < 0.001), total cholesterol (TC; WMD: -7.70 mg/dL, p = 0.006), triglycerides (WMD: -10.23 mg/dL, p = 0.0083), TC/high-density lipoprotein (WMD: -0.31 mg/dL, p < 0.001), low-density lipoprotein (WMD: -5.79 mg/dL; p < 0.001) and very-low-density lipoprotein (WMD: -5.67 mg/dL, p < 0.001) levels. However, the HOMA- ß-cell function didn't increase (WMD: -17.91, p < 0.001). Baseline maternal age (ß = 0.28, p = 0.014) and GDM diagnostic criteria (ß = 0.90, p = 0.012) were effect moderators of HOMA-IR and body mass index (BMI) (ß = 6.07, p = 0.022) and supplement type (solo versus combined) (ß = 14.99, p = 0.006) were effect moderators of triglyceride levels. Conclusion: Altogether, antenatal dietary supplements achieved control over plasma glycemic and lipid profiles in non-pharmacologically treated GDM patients. Maternal age and GDM diagnostic criteria moderated HOMA-IR levels. BMI and supplement-type moderated triglyceride levels. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-023-01369-0.

Comprehensive data on the relationship between KCNJ11 polymorphisms and gestational diabetes mellitus predisposition: a meta-analysis.

Purpose: The genetic aspect of gestational diabetes mellitus (GDM) is influenced by multiple causal genetic variants, each with different effect sizes. The KCNJ11 gene is particularly noteworthy as a potential contributor to the risk of GDM due to its role in regulating glucose-induced insulin secretion. To evaluate the association between KCNJ11 polymorphisms and GDM, a comprehensive meta-analysis was conducted to review the existing literature and quantitatively assess the correlation. Methods: A thorough search was performed on the PubMed, EMBASE, Scopus, and CNKI databases until December 25, 2023, using precise terms and keywords related to Gestational Diabetes, KCNJ11 gene, and polymorphism. Odds ratios and 95% confidence intervals were used to evaluate the relationships. The statistical analysis was conducted using Comprehensive Meta-Analysis software, and the Cochrane risk of bias assessment tool was used to determine bias presence. Results: The meta-analysis comprised 9 studies with 3108 GDM cases and 5374 controls for the rs5219 polymorphism, and 3 studies with 1209 GDM cases and 1438 controls for the rs5210 polymorphism. The pooled data indicated a noteworthy link between the rs5219 polymorphism and GDM globally and among various ethnic groups, notably in Caucasian and Asian populations. However, no substantial association was observed between the rs5210 polymorphism and GDM. Conclusions: Pooled data showed a correlation between the KCNJ11 rs5219 polymorphism and GDM susceptibility, but no association was found for the rs5210 polymorphism. Future research with larger sample sizes and more diverse populations is needed to improve result generalizability. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-024-01428-0.

The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence.

Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.

Longitudinal observation of tRNA-derived fragments profiles in gestational diabetes mellitus and its diagnostic value.

BACKGROUND: Gestational Diabetes Mellitus (GDM) poses significant risks to maternal and fetal health. Current diagnostic methods based on glucose tolerance tests have limitations for early detection. tRNA-derived small RNAs (tsRNAs) have emerged as potential molecular regulators in various diseases, including metabolic disorders. However, the diagnostic value of tsRNAs in plasma for early GDM or postpartum remains unclear. METHODS: This longitudinal study profiled the expression of tsRNAs across different gestational stages and postpartum in women with GDM (n = 40) and healthy control gestational women (HCs, n = 40). High-throughput small RNA sequencing identified candidate tsRNAs, which were then validated and correlated with clinical biochemical markers such as fasting blood glucose (FBG), HOMA-IR, and GHbA1c. RESULTS: tRF-1:32-Val-AAC-1-M6, tRF-1:31-Glu-CTC-1-M2, and tRF-1:30-Gly-CCC-1-M4 were consistently upregulated in the GDM group compared to HCs during the second trimester (p < 0.05). Only tRF-1:31-Glu-CTC-1-M2 was highly expressed during the first trimester, and tRF-1:30-Gly-CCC-1-M4 increased during postpartum. tRF-1:31-Glu-CTC-1-M2 showed a significant correlation with FBG levels in the first trimester (R = 0.317, p = 0.047). The expression of tRF-1:30-Gly-CCC-1-M4 was significantly correlated with HOMA-IR (r = 0.65, p < 0.001) and GHBA1c (r = 0.33, p = 0.037) during postpartum. A joint diagnostic model incorporating tsRNAs expression and clinical markers demonstrated enhanced predictive power for GDM (ROC AUC = 0.768). CONCLUSION: Our results revealed distinct expression patterns of specific tsRNAs in GDM, showcasing their correlation with key metabolic parameters. This underscores their promising role as biomarkers for early prediction and diagnosis of GDM. The integration of tRFs into a composite biomarker panel holds the potential to improve clinical outcomes by enabling personalized risk assessment and targeted interventions.

MTHFR gene polymorphisms in diabetes mellitus.

The methylenetetrahydrofolate reductase (MTHFR), methionine synthase (MTR), and methionine synthase reductase (MTRR) are three regulatory enzymes in the folic acid (FA) cycle play a critical role in the balance of methionine and homocysteine. MTHFR and MTRR gene polymorphisms affect the biochemical activities of enzymes, impairing the remethylation of homocysteine to methionine. In 1972, severe MTHFR deficiency resulting in homocystinuria was first reported, suggesting MTHFR involvement in the disease. MTHFR C677T polymorphism can independently increase the risk of high homocysteine (HHcy) in plasma. Elevation of homocysteine levels could increase the risk of microvascular damage, thrombosis, heart disease, etc. Vascular complications were regarded as a leading major cause of diabetes mortality, and disability increases individual health and economic burden. Diabetes mellitus (DM) is a chronic inflammatory disease, and conventional medications do not provide a complete cure for diabetes. It was essential to identify other risk factors for the intervention and prevention of diabetes. MTHFR gene polymorphism is an emerging risk factor in diabetes. Recent studies have shown that polymorphisms of the MTHFR gene play a significant role in the pathophysiology of diabetes, including inflammation and insulin resistance. This review summarizes the association between MTHER gene polymorphism and diabetes.

Association of congenital heart defects (CHD) with factors related to maternal health and pregnancy in newborns in Puerto Rico.

Background: Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States, there is a compelling need to investigate the intricate interplay among BMI, pregestational, and gestational maternal diabetes, and their potential impact on the occurrence of congenital heart defects (CHD) during neonatal development. Methods: Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico, we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020. Our assessment encompassed a range of variables, including maternal age, gestational age, BMI, pregestational diabetes, gestational diabetes, hypertension, history of abortion, and presence of preeclampsia. Results: A cohort of 673 patients was included in our study. The average maternal age was 26 years, within a range of 22 to 32 years. The mean gestational age measured 39 weeks, with a median span of 38 to 39 weeks. Of the 673 patients, 274 (41%) mothers gave birth to neonates diagnosed with CHD. Within this group, 22 cases were linked to pre-gestational diabetes, while 202 were not; 20 instances were associated with gestational diabetes, compared to 200 without; and 148 cases exhibited an overweight or obese BMI, whereas 126 displayed a normal BMI. Conclusion: We identified a statistically significant correlation between pre-gestational diabetes mellitus and the occurrence of CHD. However, our analysis did not show a statistically significant association between maternal BMI and the likelihood of CHD. These results may aid in developing effective strategies to prevent and manage CHD in neonates.

Physical Activity during Pregnancy and Risk of Gestational Diabetes Mellitus: A Meta-Review.

BACKGROUND: Nowadays, pregnant women require more individualized attention in their assistance process during pregnancy. One of the aspects that requires the most focus is the suitability of carrying out physical activity. The objective of this meta-review is to find out the effects of physical activity during pregnancy on the incidence of GDM compared to women who do not perform physical activity. METHODS: A search was conducted in Cochrane, CSIC, Ebscohost, Proquest, Pubmed, Scielo, and Scopus. The search focused on systematic reviews and meta-analyses published in the last five years. The AMSTAR-2 scale was used as a quality assessment tool for the final sample. RESULTS: A total of 18 systematic reviews and meta-analyses were included. Sixteen of them found out that physical activity during pregnancy has preventive effects for GDM compared with women who lacked physical activity. Among the studies, we found a reduction in the risk of GDM of between 24% and 38% and odds ratios ranging between 0.39 and 0.83 calculated for a 95% CI. Only two studies did not find statistically significant effects. Other variables such as type and duration of physical activity, overweight and obesity, gestational age, etc., were also considered. CONCLUSIONS: Physical activity prevents the incidence of GDM. The main characteristics that enhance this preventive effect are starting at the initial stages of pregnancy and maintaining during the whole pregnancy as well as combining strength and aerobic exercise at a low to moderate intensity.

Placental Expression of Glucose and Zinc Transporters in Women with Gestational Diabetes.

Background/Objectives: Gestational diabetes (GDM) is a metabolic disorder with altered glucose levels diagnosed in pregnant women. The pathogenesis of GDM is not fully known, but it is thought to be caused by impaired insulin production and insulin resistance induced by diabetogenic factors. The placenta may play an important role in the development of GDM. Glucose transporters (GLUTs) are responsible for the delivery of glucose into the foetal circulation. Placental zinc transporters regulate insulin and glucagon secretion, as well as gluconeogenesis and glycolysis. The aim of this study was to investigate the placental expression of GLUT3, GLUT4, GLUT7 and SLC30A8 in women with GDM. Furthermore, we evaluated whether the expression profiles of these transporters were correlated with clinical parameters. Methods: This study included 26 patients with GDM and 28 patients with normal glucose tolerance (NGT). Results: The placental expression of GLUT3 was significantly reduced in the GDM group, while the placental expression of GLUT4, GLUT7 and SLC30A8 was significantly upregulated in the GDM group. GLUT3 expression correlated significantly with body mass index (BMI) increase during pregnancy and body mass increase during pregnancy, while GLUT4 expression correlated negatively with BMI at birth. Conclusions: These results suggest the involvement of GLUT3 and GLUT4, GLUT7 and SLC30A8 in the pathogenesis of GDM.

Female-specific risk factors for cardiovascular disease: an update.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide. While it was previously believed that men have greater susceptibility to CVD, recent research suggests that women face an increased risk of CVD after the onset of menopause, primarily due to the loss of the protective effects of estrogens. Premature ovarian insufficiency (POI), polycystic ovarian syndrome (PCOS), and gestational factors, such as gestational diabetes mellitus (GDM), recurrent pregnancy loss, preterm delivery, and preeclampsia, are specific reproductive disorders that may contribute to an elevated risk of CVD at earlier ages, i.e., before the onset of menopause. This suggests that women with these conditions should be closely monitored for CVD risk factors even before reaching menopause. Such early intervention may help reduce the incidence of CVD and improve overall cardiovascular health in this population. The precise pathophysiological mechanism underlying the development of CVD in women with menopause, premature POI, PCOS, and gestational factors remains elusive. This review article seeks to elucidate the latest research on the relationship between these conditions and CVD in women, aiming to explore the underlying pathogenic mechanisms contributing to this association.

Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy.

Gestational diabetes mellitus (GDM) is a hyperglycemic state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, has low reproducibility, and results are tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to produce a result. Near-infrared (NIR) spectroscopy is a simple, fast, and low-cost analytical technique that has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and duration of analysis. Serum samples from the first trimester (before GDM diagnosis) and the second trimester (at the time of GDM diagnosis) of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 mathematical pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best models for first and second trimester achieved areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR-spectroscopy-based methods for the prediction of GDM. The developed methods allow for prediction of GDM from 10 µL of serum in only 32 min. They are simple, fast, and have a great potential for application in clinical practice, especially as alternative screening tools to the OGTT for GDM diagnosis.

Does reversible postpartum contraception reduce the risk of pregnancy condition recurrence? A longitudinal claims-based study from Maine.

PURPOSE: To estimate the effect of reversible postpartum contraception use on the risk of recurrent pregnancy condition in the subsequent pregnancy and if this effect was mediated through lengthening the interpregnancy interval (IPI). METHODS: We used data from the Maine Health Data Organization's Maine All Payer Claims dataset. Our study population was Maine women with a livebirth index pregnancy between 2007 and 2019 that was followed by a subsequent pregnancy starting within 60 months of index pregnancy delivery. We examined recurrence of three pregnancy conditions, separately, in groups that were not mutually exclusive: prenatal depression, hypertensive disorders of pregnancy (HDP), and gestational diabetes (GDM). Effective reversible postpartum contraception use was defined as any intrauterine device, implant, or moderately effective method (pills, patch, ring, injectable) initiated within 60 days of delivery. Short IPI was defined as ≤ 12 months. We used log-binomial regression models to estimate risk ratios and 95 % confidence intervals, adjusting for potential confounders. RESULTS: Approximately 41 % (11,448/28,056) of women initiated reversible contraception within 60 days of delivery, the prevalence of short IPI was 26 %, and the risk of pregnancy condition recurrence ranged from 38 % for HDP to 55 % for prenatal depression. Reversible contraception initiation within 60 days of delivery was not associated with recurrence of the pregnancy condition in the subsequent pregnancy (aRR ranged from 0.97 to 1.00); however, it was associated with lower risk of short IPI (aRR ranged from 0.67 to 0.74). CONCLUSION(S): Although initiation of postpartum reversible contraception within 60 days of delivery lengthens the IPI, our findings suggest that it does not reduce the risk of prenatal depression, HDP, or GDM recurrence. This indicates a missed opportunity for providing evidence-based healthcare and health interventions in the intrapartum period to reduce the risk of recurrence.

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.

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.