Identify gestational diabetes mellitus by deep learning model from cell-free DNA at the early gestation stage.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which has significant adverse effects on both the mother and fetus. The incidence of GDM is increasing globally, and early diagnosis is critical for timely treatment and reducing the risk of poor pregnancy outcomes. GDM is usually diagnosed and detected after 24 weeks of gestation, while complications due to GDM can occur much earlier. Copy number variations (CNVs) can be a possible biomarker for GDM diagnosis and screening in the early gestation stage. In this study, we proposed a machine-learning method to screen GDM in the early stage of gestation using cell-free DNA (cfDNA) sequencing data from maternal plasma. Five thousand and eighty-five patients from north regions of Mainland China, including 1942 GDM, were recruited. A non-overlapping sliding window method was applied for CNV coverage screening on low-coverage (~0.2×) sequencing data. The CNV coverage was fed to a convolutional neural network with attention architecture for the binary classification. The model achieved a classification accuracy of 88.14%, precision of 84.07%, recall of 93.04%, F1-score of 88.33% and AUC of 96.49%. The model identified 2190 genes associated with GDM, including DEFA1, DEFA3 and DEFB1. The enriched gene ontology (GO) terms and KEGG pathways showed that many identified genes are associated with diabetes-related pathways. Our study demonstrates the feasibility of using cfDNA sequencing data and machine-learning methods for early diagnosis of GDM, which may aid in early intervention and prevention of adverse pregnancy outcomes.

The role of ncRNA regulatory mechanisms in diseases-case on gestational diabetes.

Non-coding RNAs (ncRNAs) are a class of RNA molecules that do not have the potential to encode proteins. Meanwhile, they can occupy a significant portion of the human genome and participate in gene expression regulation through various mechanisms. Gestational diabetes mellitus (GDM) is a pathologic condition of carbohydrate intolerance that begins or is first detected during pregnancy, making it one of the most common pregnancy complications. Although the exact pathogenesis of GDM remains unclear, several recent studies have shown that ncRNAs play a crucial regulatory role in GDM. Herein, we present a comprehensive review on the multiple mechanisms of ncRNAs in GDM along with their potential role as biomarkers. In addition, we investigate the contribution of deep learning-based models in discovering disease-specific ncRNA biomarkers and elucidate the underlying mechanisms of ncRNA. This might assist community-wide efforts to obtain insights into the regulatory mechanisms of ncRNAs in disease and guide a novel approach for early diagnosis and treatment of disease.

Rheumatoid arthritis and cardiovascular complications during delivery: a United States inpatient analysis.

BACKGROUND AND AIMS: Persons with rheumatoid arthritis (RA) have an increased risk of obstetric-associated complications, as well as long-term cardiovascular (CV) risk. Hence, the aim was to evaluate the association of RA with acute CV complications during delivery admissions. METHODS: Data from the National Inpatient Sample (2004-2019) were queried utilizing ICD-9 or ICD-10 codes to identify delivery hospitalizations and a diagnosis of RA. RESULTS: A total of 12 789 722 delivery hospitalizations were identified, of which 0.1% were among persons with RA (n = 11 979). Individuals with RA, vs. those without, were older (median 31 vs. 28 years, P < .01) and had a higher prevalence of chronic hypertension, chronic diabetes, gestational diabetes mellitus, obesity, and dyslipidaemia (P < .01). After adjustment for age, race/ethnicity, comorbidities, insurance, and income, RA remained an independent risk factor for peripartum CV complications including preeclampsia [adjusted odds ratio (aOR) 1.37 (95% confidence interval 1.27-1.47)], peripartum cardiomyopathy [aOR 2.10 (1.11-3.99)], and arrhythmias [aOR 2.00 (1.68-2.38)] compared with no RA. Likewise, the risk of acute kidney injury and venous thromboembolism was higher with RA. An overall increasing trend of obesity, gestational diabetes mellitus, and acute CV complications was also observed among individuals with RA from 2004-2019. For resource utilization, length of stay and cost of hospitalization were higher for deliveries among persons with RA. CONCLUSIONS: Pregnant persons with RA had higher risk of preeclampsia, peripartum cardiomyopathy, arrhythmias, acute kidney injury, and venous thromboembolism during delivery hospitalizations. Furthermore, cardiometabolic risk factors among pregnant individuals with RA rose over this 15-year period.

Gestational diabetes and future cardiovascular diseases: associations by sex-specific genetic data.

BACKGROUND AND AIMS: Observational studies have highlighted that gestational diabetes mellitus is associated with a higher risk of cardiovascular diseases, but the causality remains unclear. Herein, the causality between genetic predisposition to gestational diabetes mellitus and the risk of cardiovascular diseases was investigated using sex-specific Mendelian randomization analysis. METHODS: Linkage disequilibrium score regression analysis and two-sample Mendelian randomization analysis were applied to infer the genetic correlation and causality, respectively. Mediation analysis was conducted using a two-step Mendelian randomization approach. Sensitivity analyses were performed to differentiate causality from pleiotropy. The genome-wide association study summary statistics for gestational diabetes mellitus were obtained from FinnGen consortium, while for cardiovascular diseases were generated based on individual-level genetic data from the UK Biobank. RESULTS: Linkage disequilibrium score regression analyses revealed that gestational diabetes mellitus had a significant genetic correlation with coronary artery disease and myocardial infarction after Benjamini-Hochberg correction in ever-pregnant women. In Mendelian randomization analyses, odds ratios (95% confidence interval) for coronary artery disease and myocardial infarction were 1.09 (1.01-1.17) and 1.12 (.96-1.31) per unit increase in the log-odds of genetic predisposition to gestational diabetes mellitus in ever-pregnant women, respectively. Further, Type 2 diabetes and hypertension were identified as mediators for the causality of genetic predisposition to gestational diabetes mellitus on coronary artery disease. In sensitivity analyses, the direction of odds ratio for the association between instrumental variables with gestational diabetes mellitus-predominant effects and the risk of coronary artery disease was consistent with the primary results in ever-pregnant women, although not statistically significant. CONCLUSIONS: This study demonstrated a suggestive causal relationship between genetic predisposition to gestational diabetes mellitus and the risk of coronary artery disease, which was mainly mediated by Type 2 diabetes and hypertension. These findings highlight targeting modifiable cardiometabolic risk factors may reduce the risk of coronary artery disease in women with a history of gestational diabetes mellitus.

Implications of pregnancy on cardiometabolic disease risk: preeclampsia and gestational diabetes.

Cardiometabolic disorders, such as obesity, insulin resistance, and hypertension, prior to and within pregnancy are increasing in prevalence worldwide. Pregnancy-associated cardiometabolic disease poses a great risk to the short- and long-term well-being of the mother and offspring. Hypertensive pregnancy, notably preeclampsia, as well as gestational diabetes are the major diseases of pregnancy growing in prevalence as a result of growing cardiometabolic disease prevalence. The mechanisms whereby obesity, diabetes, and other comorbidities lead to preeclampsia and gestational diabetes are incompletely understood and continually evolving in the literature. In addition, novel therapeutic avenues are currently being explored in these patients to offset cardiometabolic-induced adverse pregnancy outcomes in preeclamptic and gestational diabetes pregnancies. In this review, we discuss the emerging pathophysiological mechanisms of preeclampsia and gestational diabetes in the context of cardiometabolic risk as well as the most recent preclinical and clinical updates in the pathogenesis and treatment of these conditions.

Sex-Specific Alterations in Placental Proteomics Induced by Intrauterine Hyperglycemia.

Gestational diabetes mellitus (GDM) with intrauterine hyperglycemia induces a series of changes in the placenta, which have adverse effects on both the mother and the fetus. The aim of this study was to investigate the changes in the placenta in GDM and its gender differences. In this study, we established an intrauterine hyperglycemia model using ICR mice. We collected placental specimens from mice before birth for histological observation, along with tandem mass tag (TMT)-labeled proteomic analysis, which was stratified by sex. When the analysis was not segregated by sex, the GDM group showed 208 upregulated and 225 downregulated proteins in the placenta, primarily within the extracellular matrix and mitochondria. Altered biological processes included cholesterol metabolism and oxidative stress responses. After stratification by sex, the male subgroup showed a heightened tendency for immune-related pathway alterations, whereas the female subgroup manifested changes in branched-chain amino acid metabolism. Our study suggests that the observed sex differences in placental protein expression may explain the differential impact of GDM on offspring.

ChIP-seq and RNA-seq Reveal the Involvement of Histone Lactylation Modification in Gestational Diabetes Mellitus.

Lactylation is a novel post-translational modification of proteins. Although the histone lactylation modification has been reported to be involved in glucose metabolism, its role and molecular pathways in gestational diabetes mellitus (GDM) are still unclear. This study aims to elucidate the histone lactylation modification landscapes of GDM patients and explore lactylation-modification-related genes involved in GDM. We employed a combination of RNA-seq analysis and chromatin immunoprecipitation sequencing (ChIP-seq) analysis to identify upregulated differentially expressed genes (DEGs) with hyperhistone lactylation modification in GDM. We demonstrated that the levels of lactate and histone lactylation were significantly elevated in GDM patients. DEGs were involved in diabetes-related pathways, such as the PI3K-Akt signaling pathway, Jak-STAT signaling pathway, and mTOR signaling pathway. ChIP-seq analysis indicated that histone lactylation modification in the promoter regions of the GDM group was significantly changed. By integrating the results of RNA-seq and ChIP-seq analysis, we found that CACNA2D1 is a key gene for histone lactylation modification and is involved in the progression of GDM by promoting cell vitality and proliferation. In conclusion, we identified the key gene CACNA2D1, which upregulated and exhibited hypermodification of histone lactylation in GDM. These findings establish a theoretical groundwork for the targeted therapy of GDM.

Habitual carbohydrate intake is not correlated with circulating ß-hydroxybutyrate levels in pregnant women with overweight and obesity at 28 weeks' gestation.

AIMS/HYPOTHESIS: Pregnant women are advised to consume a minimum of 175 g per day of carbohydrate to meet maternal and fetal brain glucose requirements. This recommendation comes from a theoretical calculation of carbohydrate requirements in pregnancy, rather than from clinical data. This study aimed to determine whether fasting maternal ketone levels are associated with habitual carbohydrate intake in a subset of participants of the Study of PRobiotics IN Gestational diabetes (SPRING) randomised controlled trial. METHODS: Food frequency questionnaires on dietary intake during pregnancy were completed by pregnant women with overweight or obesity at 28 weeks' gestation (considering their intake from the beginning of pregnancy). Dietary intake from early pregnancy through to 28 weeks was analysed for macronutrient intake. At the same time, overnight fasting serum samples were obtained and analysed for metabolic parameters including serum ß-hydroxybutyrate, OGTTs, insulin and C-peptide. RESULTS: Fasting serum ß-hydroxybutyrate levels amongst 108 women (mean BMI 34.7 ± 6.3 kg/m2) ranged from 22.2 to 296.5 µmol/l. Median fasting ß-hydroxybutyrate levels were not different between women with high (median [IQR] 68.4 [49.1-109.2 µmol/l]) and low (65.4 [43.6-138.0 µmol/l]) carbohydrate intake in pregnancy. Fasting ß-hydroxybutyrate levels were not correlated with habitual carbohydrate intake (median 155 [126-189] g/day). The only metabolic parameter with which fasting ß-hydroxybutyrate levels were correlated was 1 h venous plasma glucose (ρ=0.23, p=0.03) during a 75 g OGTT. CONCLUSIONS/INTERPRETATION: Fasting serum ß-hydroxybutyrate levels are not associated with habitual carbohydrate intake at 28 weeks' gestation in pregnant women with overweight and obesity.

Metformin and small for gestational age babies: findings of a randomised placebo-controlled clinical trial of metformin in gestational diabetes (EMERGE).

AIMS/HYPOTHESIS: Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes because of suboptimal glucose management and glucose control and excessive weight gain. Metformin can offset these factors but is associated with small for gestational age (SGA) infants. We sought to identify risk factors for SGA infants, including the effect of metformin exposure on SGA status. METHODS: In this prespecified secondary analysis of the EMERGE trial, which evaluated the effectiveness of metformin vs placebo in treating GDM and found reduced gestational weight gain and longer time to insulin initiation with metformin use, we included women with a live-born infant and known infant birthweight and gestational age at delivery. We compared the numbers of SGA infants in both groups and explored baseline predictive factors to help identify those at highest risk of delivering an SGA infant. RESULTS: Baseline maternal characteristics were similar between SGA and non-SGA pregnancies. On multivariable-adjusted regression, no baseline maternal variables were associated with SGA status. Mothers of SGA infants were more likely to develop pre-eclampsia or gestational hypertension (18.2% vs 2.0%, p=0.001; 22.7% vs 5.4%, p=0.005, respectively); after multivariable adjustment, pre-eclampsia was positively associated with SGA status). Among SGA pregnancies, important perinatal outcomes including preterm birth, Caesarean delivery and neonatal care unit admission did not differ between the metformin and placebo groups (20.0% vs 14.3%, p=1.00; 50.0% vs 28.6%, p=0.25; 13.3% vs 42.9%, p=0.27, respectively). CONCLUSIONS/INTERPRETATION: Pre-eclampsia was strongly associated with SGA infants. Metformin-exposed SGA infants did not display a more severe SGA phenotype than infants treated with placebo. TRIAL REGISTRATION: Clinical Trials.gov NCT02980276; EudraCT number: 2016-001644-19.

Metabolomic and genetic architecture of gestational diabetes subtypes.

AIMS/HYPOTHESIS: Physiological gestational diabetes mellitus (GDM) subtypes that may confer different risks for adverse pregnancy outcomes have been defined. The aim of this study was to characterise the metabolome and genetic architecture of GDM subtypes to address the hypothesis that they differ between GDM subtypes. METHODS: This was a cross-sectional study of participants in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study who underwent an OGTT at approximately 28 weeks' gestation. GDM was defined retrospectively using International Association of Diabetes and Pregnancy Study Groups/WHO criteria, and classified as insulin-deficient GDM (insulin secretion <25th percentile with preserved insulin sensitivity) or insulin-resistant GDM (insulin sensitivity <25th percentile with preserved insulin secretion). Metabolomic analyses were performed on fasting and 1 h serum samples in 3463 individuals (576 with GDM). Genome-wide genotype data were obtained for 8067 individuals (1323 with GDM). RESULTS: Regression analyses demonstrated striking differences between the metabolomes for insulin-deficient or insulin-resistant GDM compared to those with normal glucose tolerance. After adjustment for covariates, 33 fasting metabolites, including 22 medium- and long-chain acylcarnitines, were uniquely associated with insulin-deficient GDM; 23 metabolites, including the branched-chain amino acids and their metabolites, were uniquely associated with insulin-resistant GDM; two metabolites (glycerol and 2-hydroxybutyrate) were associated with the same direction of association with both subtypes. Subtype differences were also observed 1 h after a glucose load. In genome-wide association studies, variants within MTNR1B (rs10830963, p=3.43×10-18, OR 1.55) and GCKR (rs1260326, p=5.17×10-13, OR 1.43) were associated with GDM. Variants in GCKR (rs1260326, p=1.36×10-13, OR 1.60) and MTNR1B (rs10830963, p=1.22×10-9, OR 1.49) demonstrated genome-wide significant association with insulin-resistant GDM; there were no significant associations with insulin-deficient GDM. The lead SNP in GCKR, rs1260326, was associated with the levels of eight of the 25 fasting metabolites that were associated with insulin-resistant GDM and ten of 41 1 h metabolites that were associated with insulin-resistant GDM. CONCLUSIONS/INTERPRETATION: This study demonstrates that physiological GDM subtypes differ in their metabolome and genetic architecture. These findings require replication in additional cohorts, but suggest that these differences may contribute to subtype-related adverse pregnancy outcomes.

Lifetime history of gestational diabetes and cognitive function in parous women in midlife.

AIMS/HYPOTHESIS: We aimed to determine whether a history of gestational diabetes mellitus (GDM) is associated with cognitive function in midlife. METHODS: We conducted a secondary data analysis of the prospective Nurses' Health Study II. From 1989 to 2001, and then in 2009, participants reported their history of GDM. A subset participated in a cognition sub-study in 2014-2019 (wave 1) or 2018-2022 (wave 2). We included 15,906 parous participants (≥1 birth at ≥18 years) who completed a cognitive assessment and were free of CVD, cancer and diabetes before their first birth. The primary exposure was a history of GDM. Additionally, we studied exposure to GDM and subsequent type 2 diabetes mellitus (neither GDM nor type 2 diabetes, GDM only, type 2 diabetes only or GDM followed by type 2 diabetes) and conducted mediation analysis by type 2 diabetes. The outcomes were composite z scores measuring psychomotor speed/attention, learning/working memory and global cognition obtained with the Cogstate brief battery. Mean differences (ß and 95% CI) in cognitive function by GDM were estimated using linear regression. RESULTS: The 15,906 participants were a mean of 62.0 years (SD 4.9) at cognitive assessment, and 4.7% (n=749) had a history of GDM. In models adjusted for age at cognitive assessment, race and ethnicity, education, wave of enrolment in the cognition sub-study, socioeconomic status and pre-pregnancy characteristics, women with a history of GDM had lower performance in psychomotor speed/attention (ß -0.08; 95% CI -0.14, -0.01) and global cognition (ß -0.06; 95% CI -0.11, -0.01) than those without a history of GDM. The lower cognitive performance in women with GDM was only partially explained by the development of type 2 diabetes. CONCLUSIONS/INTERPRETATION: Women with a history of GDM had poorer cognition than those without GDM. If replicated, our findings support future research on early risk modification strategies for women with a history of GDM as a potential avenue to decrease their risk of cognitive impairment.

Genome-wide association and Mendelian randomisation analysis among 30,699 Chinese pregnant women identifies novel genetic and molecular risk factors for gestational diabetes and glycaemic traits.

AIMS/HYPOTHESIS: Gestational diabetes mellitus (GDM) is the most common disorder in pregnancy; however, its underlying causes remain obscure. This study aimed to investigate the genetic and molecular risk factors contributing to GDM and glycaemic traits. METHODS: We collected non-invasive prenatal test (NIPT) sequencing data along with four glycaemic and 55 biochemical measurements from 30,699 pregnant women during a 2 year period at Shenzhen Baoan Women's and Children's Hospital in China. Genome-wide association studies (GWAS) were conducted between genotypes derived from NIPTs and GDM diagnosis, baseline glycaemic levels and glycaemic levels after glucose challenges. In total, 3317 women were diagnosed with GDM, while 19,565 served as control participants. The results were replicated using two independent cohorts. Additionally, we performed one-sample Mendelian randomisation to explore potential causal associations between the 55 biochemical measurements and risk of GDM and glycaemic levels. RESULTS: We identified four genetic loci significantly associated with GDM susceptibility. Among these, MTNR1B exhibited the highest significance (rs10830963-G, OR [95% CI] 1.57 [1.45, 1.70], p=4.42×10-29), although its effect on type 2 diabetes was modest. Furthermore, we found 31 genetic loci, including 14 novel loci, that were significantly associated with the four glycaemic traits. The replication rates of these associations with GDM, fasting plasma glucose levels and 0 h, 1 h and 2 h OGTT glucose levels were 4 out of 4, 6 out of 9, 10 out of 11, 5 out of 7 and 4 out of 4, respectively. Mendelian randomisation analysis suggested that a genetically regulated higher lymphocytes percentage and lower white blood cell count, neutrophil percentage and absolute neutrophil count were associated with elevated glucose levels and an increased risk of GDM. CONCLUSIONS/INTERPRETATION: Our findings provide new insights into the genetic basis of GDM and glycaemic traits during pregnancy in an East Asian population and highlight the potential role of inflammatory pathways in the aetiology of GDM and variations in glycaemic levels. DATA AVAILABILITY: Summary statistics for GDM; fasting plasma glucose; 0 h, 1 h and 2h OGTT; and the 55 biomarkers are available in the GWAS Atlas (study accession no.: GVP000001, https://ngdc.cncb.ac.cn/gwas/browse/GVP000001) .

Analysis of early-pregnancy metabolome in early- and late-onset gestational diabetes reveals distinct associations with maternal overweight.

AIMS/HYPOTHESIS: It is not known whether the early-pregnancy metabolome differs in patients with early- vs late-onset gestational diabetes mellitus (GDM) stratified by maternal overweight. The aims of this study were to analyse correlations between early-pregnancy metabolites and maternal glycaemic and anthropometric characteristics, and to identify early-pregnancy metabolomic alterations that characterise lean women (BMI <25 kg/m2) and women with overweight (BMI ≥25 kg/m2) with early-onset GDM (E-GDM) or late-onset GDM (L-GDM). METHODS: We performed a nested case-control study within the population-based prospective Early Diagnosis of Diabetes in Pregnancy cohort, comprising 210 participants with GDM (126 early-onset, 84 late-onset) and 209 normoglycaemic control participants matched according to maternal age, BMI class and primiparity. Maternal weight, height and waist circumference were measured at 8-14 weeks' gestation. A 2 h 75 g OGTT was performed at 12-16 weeks' gestation (OGTT1), and women with normal results underwent repeat testing at 24-28 weeks' gestation (OGTT2). Comprehensive metabolomic profiling of fasting serum samples, collected at OGTT1, was performed by untargeted ultra-HPLC-MS. Linear models were applied to study correlations between early-pregnancy metabolites and maternal glucose concentrations during OGTT1, fasting insulin, HOMA-IR, BMI and waist circumference. Early-pregnancy metabolomic features for GDM subtypes (participants stratified by maternal overweight and gestational timepoint at GDM onset) were studied using linear and multivariate models. The false discovery rate was controlled using the Benjamini-Hochberg method. RESULTS: In the total cohort (n=419), the clearest correlation patterns were observed between (1) maternal glucose concentrations and long-chain fatty acids and medium- and long-chain acylcarnitines; (2) maternal BMI and/or waist circumference and long-chain fatty acids, medium- and long-chain acylcarnitines, phospholipids, and aromatic and branched-chain amino acids; and (3) HOMA-IR and/or fasting insulin and L-tyrosine, certain long-chain fatty acids and phospholipids (q<0.001). Univariate analyses of GDM subtypes revealed significant differences (q<0.05) for seven non-glucose metabolites only in overweight women with E-GDM compared with control participants: linolenic acid, oleic acid, docosapentaenoic acid, docosatetraenoic acid and lysophosphatidylcholine 20:4/0:0 abundances were higher, whereas levels of specific phosphatidylcholines (P-16:0/18:2 and 15:0/18:2) were lower. However, multivariate analyses exploring the early-pregnancy metabolome of GDM subtypes showed differential clustering of acylcarnitines and long-chain fatty acids between normal-weight and overweight women with E- and L-GDM. CONCLUSIONS/INTERPRETATION: GDM subtypes show distinct early-pregnancy metabolomic features that correlate with maternal glycaemic and anthropometric characteristics. The patterns identified suggest early-pregnancy disturbances of maternal lipid metabolism, with most alterations observed in overweight women with E-GDM. Our findings highlight the importance of maternal adiposity as the primary target for prevention and treatment.

Type 2 diabetes and succinate: unmasking an age-old molecule.

Beyond their conventional roles in intracellular energy production, some traditional metabolites also function as extracellular messengers that activate cell-surface G-protein-coupled receptors (GPCRs) akin to hormones and neurotransmitters. These signalling metabolites, often derived from nutrients, the gut microbiota or the host's intermediary metabolism, are now acknowledged as key regulators of various metabolic and immune responses. This review delves into the multi-dimensional aspects of succinate, a dual metabolite with roots in both the mitochondria and microbiome. It also connects the dots between succinate's role in the Krebs cycle, mitochondrial respiration, and its double-edge function as a signalling transmitter within and outside the cell. We aim to provide an overview of the role of the succinate-succinate receptor 1 (SUCNR1) axis in diabetes, discussing the potential use of succinate as a biomarker and the novel prospect of targeting SUCNR1 to manage complications associated with diabetes. We further propose strategies to manipulate the succinate-SUCNR1 axis for better diabetes management; this includes pharmacological modulation of SUCNR1 and innovative approaches to manage succinate concentrations, such as succinate administration and indirect strategies, like microbiota modulation. The dual nature of succinate, both in terms of origins and roles, offers a rich landscape for understanding the intricate connections within metabolic diseases, like diabetes, and indicates promising pathways for developing new therapeutic strategies.

Maternal and neonatal outcomes according to the timing of diagnosis of hyperglycaemia in pregnancy: a nationwide cross-sectional study of 695,912 deliveries in France in 2018.

AIMS/HYPOTHESIS: We aimed to assess maternal-fetal outcomes according to various subtypes of hyperglycaemia in pregnancy. METHODS: We used data from the French National Health Data System (Système National des Données de Santé), which links individual data from the hospital discharge database and the French National Health Insurance information system. We included all deliveries after 22 gestational weeks (GW) in women without pre-existing diabetes recorded in 2018. Women with hyperglycaemia were classified as having overt diabetes in pregnancy or gestational diabetes mellitus (GDM), then categorised into three subgroups according to their gestational age at the time of GDM diagnosis: before 22 GW (GDM<22); between 22 and 30 GW (GDM22-30); and after 30 GW (GDM>30). Adjusted prevalence ratios (95% CI) for the outcomes were estimated after adjusting for maternal age, gestational age and socioeconomic status. Due to the multiple tests, we considered an association to be statistically significant according to the Holm-Bonferroni procedure. To take into account the potential immortal time bias, we performed analyses on deliveries at ≥31 GW and deliveries at ≥37 GW. RESULTS: The study population of 695,912 women who gave birth in 2018 included 84,705 women (12.2%) with hyperglycaemia in pregnancy: overt diabetes in pregnancy, 0.4%; GDM<22, 36.8%; GDM22-30, 52.4%; and GDM>30, 10.4%. The following outcomes were statistically significant after Holm-Bonferroni adjustment for deliveries at ≥31 GW using GDM22-30 as the reference. Caesarean sections (1.54 [1.39, 1.72]), large-for-gestational-age (LGA) infants (2.00 [1.72, 2.32]), Erb's palsy or clavicle fracture (6.38 [2.42, 16.8]), preterm birth (1.84 [1.41, 2.40]) and neonatal hypoglycaemia (1.98 [1.39, 2.83]) were more frequent in women with overt diabetes. Similarly, LGA infants (1.10 [1.06, 1.14]) and Erb's palsy or clavicle fracture (1.55 [1.22, 1.99]) were more frequent in GDM<22. LGA infants (1.44 [1.37, 1.52]) were more frequent in GDM>30. Finally, women without hyperglycaemia in pregnancy were less likely to have preeclampsia or eclampsia (0.74 [0.69, 0.79]), Caesarean section (0.80 [0.79, 0.82]), pregnancy and postpartum haemorrhage (0.93 [0.89, 0.96]), LGA neonate (0.67 [0.65, 0.69]), premature neonate (0.80 [0.77, 0.83]) and neonate with neonatal hypoglycaemia (0.73 [0.66, 0.82]). Overall, the results were similar for deliveries at ≥37 GW. Although the estimation of the adjusted prevalence ratio of perinatal death was five times higher (5.06 [1.87, 13.7]) for women with overt diabetes, this result was non-significant after Holm-Bonferroni adjustment. CONCLUSIONS/INTERPRETATION: Compared with GDM22-30, overt diabetes, GDM<22 and, to a lesser extent, GDM>30 were associated with poorer maternal-fetal outcomes.

Identification and validation of gestational diabetes subgroups by data-driven cluster analysis.

AIMS/HYPOTHESIS: Gestational diabetes mellitus (GDM) is a heterogeneous condition. Given such variability among patients, the ability to recognise distinct GDM subgroups using routine clinical variables may guide more personalised treatments. Our main aim was to identify distinct GDM subtypes through cluster analysis using routine clinical variables, and analyse treatment needs and pregnancy outcomes across these subgroups. METHODS: In this cohort study, we analysed datasets from a total of 2682 women with GDM treated at two central European hospitals (1865 participants from Charité University Hospital in Berlin and 817 participants from the Medical University of Vienna), collected between 2015 and 2022. We evaluated various clustering models, including k-means, k-medoids and agglomerative hierarchical clustering. Internal validation techniques were used to guide best model selection, while external validation on independent test sets was used to assess model generalisability. Clinical outcomes such as specific treatment needs and maternal and fetal complications were analysed across the identified clusters. RESULTS: Our optimal model identified three clusters from routinely available variables, i.e. maternal age, pre-pregnancy BMI (BMIPG) and glucose levels at fasting and 60 and 120 min after the diagnostic OGTT (OGTT0, OGTT60 and OGTT120, respectively). Cluster 1 was characterised by the highest OGTT values and obesity prevalence. Cluster 2 displayed intermediate BMIPG and elevated OGTT0, while cluster 3 consisted mainly of participants with normal BMIPG and high values for OGTT60 and OGTT120. Treatment modalities and clinical outcomes varied among clusters. In particular, cluster 1 participants showed a much higher need for glucose-lowering medications (39.6% of participants, compared with 12.9% and 10.0% in clusters 2 and 3, respectively, p<0.0001). Cluster 1 participants were also at higher risk of delivering large-for-gestational-age infants. Differences in the type of insulin-based treatment between cluster 2 and cluster 3 were observed in the external validation cohort. CONCLUSIONS/INTERPRETATION: Our findings confirm the heterogeneity of GDM. The identification of subgroups (clusters) has the potential to help clinicians define more tailored treatment approaches for improved maternal and neonatal outcomes.

Placental mitochondrial impairment and its association with maternal metabolic dysfunction.

The placenta plays an essential role in pregnancy, leading to proper fetal development and growth. As an organ with multiple physiological functions for both mother and fetus, it is a highly energetic and metabolically demanding tissue. Mitochondrial physiology plays a crucial role in the metabolism of this organ and thus any alteration leading to mitochondrial dysfunction has a severe outcome in the development of the fetus. Pregnancy-related pathological states with a mitochondrial dysfunction outcome include preeclampsia and gestational diabetes mellitus. In this review, we address the role of mitochondrial morphology, metabolism and physiology of the placenta during pregnancy, highlighting the roles of the cytotrophoblast and syncytiotrophoblast. We also describe the relationship between preeclampsia, gestational diabetes, gestational diabesity and pre-pregnancy maternal obesity with mitochondrial dysfunction.

The RhoA guanine exchange factor ABR: a glucose-sensitive mediator of actin reorganization in feto-placental arterial endothelial cells altered by gestational diabetes mellitus.

In utero exposure to gestational diabetes mellitus (GDM) programs the fetus, increasing offspring risk for endothelial dysfunction and cardiovascular disease later in life. Hyperglycaemia is widely recognized as the driving force of diabetes-induced programming. We have previously shown that GDM exposure alters DNA methylation and gene expression associated with actin remodelling in primary feto-placental arterial endothelial cells (fpEC). Thus, we hypothesized that hyperglycaemic insults underlie programmed changes in fpEC morphology and actin organization by GDM. Therefore, arterial fpECs isolated after normal and GDM pregnancy, as well as normal fpECs that were exposed to hyperglycaemia in vitro, were analysed for the effect of GDM and hyperglycaemia on actin organization and network formation. Integration of gene expression and DNA methylation data identified the RhoA activator active BCR-related (ABR) as programmed by GDM and altered by in vitro hyperglycaemia. ABR silencing in GDM-exposed cells reduced RhoA activity by 34 ± 26% (P = 0.033) and restored normal fpEC phenotype. In fact, in vitro hyperglycaemia induced a similar fpEC phenotype as intrauterine exposure to GDM, i.e. round morphology and increased network formation on Matrigel by 34 ± 33% (P = 0.022) vs. 22 ± 20% for GDM (P = 0.004). Thus, we identified ABR as a novel glucose sensitive regulator of actin organization and cell shape, programmed by GDM and upregulated by hyperglycaemia. Identification of mechanisms induced by hyperglycaemia and affecting endothelial function in the long term will contribute to understanding GDM-induced programming of offspring endothelial dysfunction and cardiovascular disease. Future studies could focus on investigating the prevention or reversal of such malprogramming. KEY POINTS: In utero exposure to gestational diabetes mellitus (GDM) affects future health of the offspring, with an increased risk for endothelial dysfunction and cardiovascular disease in later life. GDM alters DNA methylation and expression of ABR in feto-placental arterial endothelial cells (fpEC), a model for endothelial cells exposed to the intrauterine environment of the fetus. GDM phenotype of fpECs is also induced by hyperglycaemia in vitro, and is characterized by altered actin organization and cell shape, which can be restored by ABR silencing. Revealing the cellular mechanisms induced by GDM and hyperglycaemia is important for understanding the mechanisms of how these conditions disturb endothelial function in the offspring.

Beta-cell compensation and gestational diabetes.

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance in pregnant women without a previous diagnosis of diabetes. While the etiology of GDM remains elusive, the close association of GDM with increased maternal adiposity and advanced gestational age implicates insulin resistance as a culpable factor for the pathogenesis of GDM. Pregnancy is accompanied by the physiological induction of insulin resistance in the mother secondary to maternal weight gain. This effect serves to spare blood glucose for the fetus. To overcome insulin resistance, maternal ß-cells are conditioned to release more insulin into the blood. Such an adaptive response, termed ß-cell compensation, is essential for maintaining normal maternal metabolism. ß-cell compensation culminates in the expansion of ß-cell mass and augmentation of ß-cell function, accounting for increased insulin synthesis and secretion. As a result, a vast majority of mothers are protected from developing GDM during pregnancy. In at-risk pregnant women, ß-cells fail to compensate for maternal insulin resistance, contributing to insulin insufficiency and GDM. However, gestational ß-cell compensation ensues in early pregnancy, prior to the establishment of insulin resistance in late pregnancy. How ß-cells compensate for pregnancy and what causes ß-cell failure in GDM are subjects of investigation. In this mini-review, we will provide clinical and preclinical evidence that ß-cell compensation is pivotal for overriding maternal insulin resistance to protect against GDM. We will highlight key molecules whose functions are critical for integrating gestational hormones to ß-cell compensation for pregnancy. We will provide mechanistic insights into ß-cell decompensation in the etiology of GDM.

Association between SARS-CoV-2 infection during pregnancy and gestational diabetes: a claims-based cohort study.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) may be associated with gestational diabetes mellitus (GDM); however, evidence is limited by sample sizes and lack of control groups. METHODS: To assess the GDM risk after COVID-19 in pregnancy, we constructed a retrospective cohort of pregnancies ending March 2020-October 2022 using medical claims. People with COVID-19 diagnosis claims from conception to 21 gestational weeks (n = 57,675) were matched 1:2 to those without COVID-19 during pregnancy (n =115,350) by age-range, pregnancy start month, and encounter year-month. GDM (claim ≥23 gestational weeks) relative risk and risk difference overall, by race and ethnicity, and variant period were estimated using log-binomial models. RESULTS: GDM risk was higher among those with COVID-19 during pregnancy compared to those without (adjusted risk ratio, aRR = 1.12, 95% CI: 1.08-1.15). GDM risk was significantly associated with COVID-19 in non-Hispanic (NH) White (aRR = 1.08, 95% CI: 1.04-1.14), NH Black (aRR=1.15, 95% CI: 1.07-1.24), and Hispanic (aRR = 1.17, 95% CI: 1.10-1.24) groups. GDM risk was significantly higher during pre-Delta (aRR = 1.17, 95% CI: 1.11-1.24) as compared to Omicron (aRR = 1.07, 95% CI: 1.02-1.13) periods, but neither differed from the Delta period (aRR = 1.10, 95% CI: 1.04-1.17). The adjusted risk difference was 0-2% for all models. CONCLUSIONS: COVID-19 during pregnancy was modestly associated with GDM in claims-based data, especially during earlier SARS-CoV-2 variant periods. As these associations are based on COVID-19 in claims data, studies employing systematic testing are warranted.