Maternal Metabolites Associated With Gestational Diabetes Mellitus and a Postpartum Disorder of Glucose Metabolism

Sensitive months for green spaces' impact on macrosomia and interaction with air pollutants: A birth cohort study

Macrosomia poses significant health risks to mother and fetuses, yet the protective sensitive window for the effects of green space resources on the risk of macrosomia remains unexplored. This study identified sensitive windows of green space exposure and examined the interactions with air pollutants. In a study of 221,380 full-term newborns delivered at the Hospital, from 2017 to 2021, Normalized Difference Vegetation Index (NDVI) and atmospheric pollutant concentrations were matched to participants based on their residences in the Ningxia region. A Cox proportional hazards model was utilized to estimate the association between green space exposure and macrosomia and analyze the differences between the macrosomia (<4500 g) and macrosomia (≥4500 g) groups. Green space exposure for each month of pregnancy was employed to identify possible sensitive windows. Possible interactions between green spaces and air pollutants were tested on additive and multiplicative scales. Across 250, 500, 1000, and 2000-m buffers, increased NDVI exposure and range throughout the pregnancy were linked to a lower macrosomia risk, with the strongest association in the macrosomia (≥4500 g) group. The key window for the protective effect of green spaces was in late pregnancy, with the most pronounced protective effect noted in the 9th month of pregnancy. We also found a consistent combined effect between low green space and the air pollutants (NO2 and SO2). The research highlights the beneficial impact of increased green space during late pregnancy and the combined effect of low green space and elevated air pollutant levels on macrosomia risk, which can support government initiatives in urban green space development and public health protection.

Metabolic profiling identifies potential biomarkers associated with progression from gestational diabetes mellitus to prediabetes postpartum

The current study aims to identify potential metabolic biomarkers that predict the progression to prediabetes in women with a history of gestational diabetes mellitus (GDM). We constructed a prediabetes group ( n = 42) and a control group ( n = 40) based on a2-hour 75 g oral glucose tolerance test for women with a history of GDM from six weeks to six months postpartum, and collected their clinical data and biochemical test results. We performed the plasma metabolomics analysis of the subjects at the fasting and 2-hour post-load time points by using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS). We found that the prediabetes group was older and had higher 2-hour post-load glucose levels during pregnancy than the control group. The metabolomic analysis identified 164 differential metabolites between the groups. Compared with the control group, 15 metabolites in the prediabetes group exhibited consistent change trends at both time points, including three increased and 12 decreased metabolites. By building a prediction model of the progression from GDM to prediabetes, we found a combination of three clinical markers yielded an area under thecurve (AUC) of 0.71 (95% confidence interval [CI], 0.60-0.82). We also assessed the discriminative power of the panel of 15 metabolites for distinguishing between postpartum prediabetes and normal glucose tolerance of the subjects at the fasting (AUC, 0.98; 95% CI, 0.94-1.00) and 2-hour post-load (AUC, 0.99; 95% CI, 0.97-1.00) time points. The metabolic pathway analysis indicated that energy metabolism and branched-chain amino acids played a role in the development of prediabetes in women with a history of GDM during early postpartum. In conclusion, this study identified potential metabolic biomarkers and pathways associated with the progression from GDM to prediabetes in the early postpartum period. A panel of 15 metabolites showed promising discriminative power for distinguishing between postpartum prediabetes and normal glucose tolerance. These findings provide insights into the underlying pathophysiology of this transition and suggest the feasibility of developing a metabolic profiling test for the early identification of women at high risk of prediabetes following GDM.

Serum metabolite profiles of thyroid autoimmunity patients in early pregnancy

Background

Research on serum metabolite profiles in thyroid autoimmunity (TAI) patients during early pregnancy is currently limited.

Aim & Methods

The current study aimed to identify differential serum metabolites and assess the relationship between pregnancy outcomes and metabolic abnormalities in individuals with TAI. This research included 26 pregnant women with TAI and 30 healthy controls (HC). We employed a liquid chromatograph mass spectrometer (LC-MS) to analyze changes between the two groups.

Results

Newborns in the TAI patients had lower birth weights than those in the control group (P = 0.007). We identified 92 differential metabolites (including 50 upregulated and 42 downregulated) belonging to amino acids, fatty acyls, glycerophosphocholines, steroid and other categories and four significantly enrichment Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways including taurine and hypotaurine metabolism, citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolism and 2-oxocarboxylic acid metabolism. We further identified 15 characteristic metabolites (6-Methylquinoline, D-erythrose 4-phosphate, 4-Hydroxyisoleucine, phosphatidylcholine (PC)(16:2e/16:0), N3,N4-Dimethyl-L-arginine, N-desmethyltramadol, 3-Methoxybenzaldehyde, sphingomyelin (SM)(d14:3/28:2), gamma-Glutamylleucine, NSI-189, 3-(1-cyano-1,2-dihydroisoquinolin-2-yl)-3-oxopropyl propionate, lysophosphatidylinositol (LPI) 16:0, cis-Aconitic acid, polyamide (PA)(18:1/18:2) and fatty acyl esters of hydroxy fatty acid (FAHFA)(17:0/18:0)) using least absolute shrinkage and selection operator (LASSO) regression. Correlation analyses revealed that 6-Methylquinoline, D-erythrose 4-phosphate, gamma-Glutamylleucine, and LPI 16:0 exhibited a positive correlation with anemia before delivery, while 3-(1-cyano-1,2-dihydroisoquinolin-2-yl)-3-oxopropyl propionate had a negative correlation. LPI 16:0 displayed a positive correlation with uric acid (UA) during both middle and late pregnancy, whereas 3-Methoxybenzaldehyde exhibited a negative correlation with UA in late pregnancy. Cis-Aconitic acid showed a positive correlation with fasting blood glucose (FBG) in middle pregnancy. Conversely, 6-Methylquinoline and 4-Hydroxyisoleucine had a negative correlation with birth weight. Thyroid autoantibodies were found to be associated with 14 metabolites identified using LASSO, with the exception of PA (18:1/18:2).

Conclusions

Our findings provide new evidence supporting the early screening of serum metabolites and their potential for predicting adverse pregnancy outcomes in women with TAI.

Cellular and Molecular Pathophysiology of Gestational Diabetes

Gestational diabetes (GD) is a metabolic disorder characterized by glucose intolerance during pregnancy, significantly impacting maternal and fetal health. Its global prevalence is approximately 14%, with risk factors including obesity, family history of diabetes, advanced maternal age, and ethnicity, which are linked to cellular and molecular disruptions in glucose regulation and insulin resistance. GD is associated with short- and long-term complications for both the mother and the newborn. For mothers, GD increases the risk of developing type 2 diabetes, cardiovascular diseases, and metabolic syndrome. In the offspring, exposure to GD in utero predisposes them to obesity, glucose intolerance, and metabolic disorders later in life. This review aims to elucidate the complex cellular and molecular mechanisms underlying GD to inform the development of effective therapeutic strategies. A systematic review was conducted using medical subject headings (MeSH) terms related to GD's cellular and molecular pathophysiology. Inclusion criteria encompassed original studies, systematic reviews, and meta-analyses focusing on GD's impact on maternal and fetal health, adhering to PRISMA guidelines. Data extraction captured study characteristics, maternal and fetal outcomes, key findings, and conclusions. GD disrupts insulin signaling pathways, leading to impaired glucose uptake and insulin resistance. Mitochondrial dysfunction reduces ATP production and increases reactive oxygen species, exacerbating oxidative stress. Hormonal influences, chronic inflammation, and dysregulation of the mammalian target of rapamycin (mTOR) pathway further impair insulin signaling. Gut microbiota alterations, gene expression, and epigenetic modifications play significant roles in GD. Ferroptosis and placental dysfunction primarily contribute to intrauterine growth restriction. Conversely, fetal macrosomia arises from maternal hyperglycemia and subsequent fetal hyperinsulinemia, resulting in excessive fetal growth. The chronic inflammatory state and oxidative stress associated with GD exacerbate these complications, creating a hostile intrauterine environment. GD's complex pathophysiology involves multiple disruptions in insulin signaling, mitochondrial function, inflammation, and oxidative stress. Effective management requires early detection, preventive strategies, and international collaboration to standardize care and improve outcomes for mothers and babies.

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.

Multigenerational diabetes mellitus

Gestational diabetes (GDM) changes the maternal metabolic and uterine environment, thus increasing the risk of short- and long-term adverse outcomes for both mother and child. Children of mothers who have GDM during their pregnancy are more likely to develop Type 2 Diabetes (T2D), early-onset cardiovascular disease and GDM when they themselves become pregnant, perpetuating a multigenerational increased risk of metabolic disease. The negative effect of GDM is exacerbated by maternal obesity, which induces a greater derangement of fetal adipogenesis and growth. Multiple factors, including genetic, epigenetic and metabolic, which interact with lifestyle factors and the environment, are likely to contribute to the development of GDM. Genetic factors are particularly important, with 30% of women with GDM having at least one parent with T2D. Fetal epigenetic modifications occur in response to maternal GDM, and may mediate both multi- and transgenerational risk. Changes to the maternal metabolome in GDM are primarily related to fatty acid oxidation, inflammation and insulin resistance. These might be effective early biomarkers allowing the identification of women at risk of GDM prior to the development of hyperglycaemia. The impact of the intra-uterine environment on the developing fetus, "developmental programming", has a multisystem effect, but its influence on adipogenesis is particularly important as it will determine baseline insulin sensitivity, and the response to future metabolic challenges. Identifying the critical window of metabolic development and developing effective interventions are key to our ability to improve population metabolic health.

The role of lipid dysregulation in gestational diabetes mellitus: Early prediction and postpartum prognosis

Gestational diabetes mellitus (GDM) is a pathological condition during pregnancy characterized by impaired glucose tolerance, and the failure of pancreatic beta-cells to respond appropriately to an increased insulin demand. However, while the majority of women with GDM will return to normoglycemia after delivery, they have up to a seven times higher risk of developing type 2 diabetes during midlife, compared with those with no history of GDM. Gestational diabetes mellitus also increases the risk of multiple metabolic disorders, including non-alcoholic fatty liver disease, obesity, and cardiovascular diseases. Lipid metabolism undergoes significant changes throughout the gestational period, and lipid dysregulation is strongly associated with GDM and the progression to future type 2 diabetes. In addition to common lipid variables, discovery-based omics techniques, such as metabolomics and lipidomics, have identified lipid biomarkers that correlate with GDM. These lipid species also show considerable potential in predicting the onset of GDM and subsequent type 2 diabetes post-delivery. This review aims to update the current knowledge of the role that lipids play in the onset of GDM, with a focus on potential lipid biomarkers or metabolic pathways. These biomarkers may be useful in establishing predictive models to accurately predict the future onset of GDM and type 2 diabetes, and early intervention may help to reduce the complications associated with GDM.

Metabolomic Profiles of Nonobese and Obese Women With Gestational Diabetes

CONTEXT

In non-pregnant population, nonobese individuals with obesity-related metabolome have increased risk for type 2 diabetes and cardiovascular diseases. The risk of these diseases is also increased after gestational diabetes.

OBJECTIVE

This work aimed to examine whether nonobese (body mass index [BMI] < 30) and obese (BMI ≥ 30) women with gestational diabetes mellitus (GDM) and obese non-GDM women differ in metabolomic profiles from nonobese non-GDM controls.

METHODS

Levels of 66 metabolic measures were assessed in early (median 13, IQR 12.4-13.7 gestation weeks), and across early, mid (20, 19.3-23.0), and late (28, 27.0-35.0) pregnancy blood samples in 755 pregnant women from the PREDO and RADIEL studies. The independent replication cohort comprised 490 pregnant women.

RESULTS

Nonobese and obese GDM, and obese non-GDM women differed similarly from the controls across early, mid, and late pregnancy in 13 measures, including very low-density lipoprotein-related measures, and fatty acids. In 6 measures, including fatty acid (FA) ratios, glycolysis-related measures, valine, and 3-hydroxybutyrate, the differences between obese GDM women and controls were more pronounced than the differences between nonobese GDM or obese non-GDM women and controls. In 16 measures, including HDL-related measures, FA ratios, amino acids, and inflammation, differences between obese GDM or obese non-GDM women and controls were more pronounced than the differences between nonobese GDM women and controls. Most differences were evident in early pregnancy, and in the replication cohort were more often in the same direction than would be expected by chance alone.

CONCLUSION

Differences between nonobese and obese GDM, or obese non-GDM women and controls in metabolomic profiles may allow detection of high-risk women for timely targeted preventive interventions.

Distinct maternal metabolites are associated with obesity and glucose-insulin axis in the first trimester of pregnancy

BACKGROUND/OBJECTIVES

Obesity in pregnancy associates with changes in the glucose-insulin axis. We hypothesized that these changes affect the maternal metabolome already in the first trimester of human pregnancy and, thus, aimed to identify these metabolites.

PATIENTS/METHODS

We performed untargeted metabolomics (HPLC-MS/MS) on maternal serum (n = 181, gestational weeks 4+0-11+6). For further analysis, we included only non-smoking women as assessed by serum cotinine levels (ELISA) (n = 111). In addition to body mass index (BMI) and leptin as measures of obesity and adiposity, we metabolically phenotyped women by their fasting glucose, C-peptide and insulin sensitivity (ISHOMA index). To identify metabolites (outcome) associated with BMI, leptin, glucose, C-peptide and/or ISHOMA (exposures), we used a combination of univariable and multivariable regression analyses with multiple confounders and machine learning methods (Partial Least Squares Discriminant Analysis, Random Forest and Support Vector Machine). Additional statistical tests confirmed robustness of results. Furthermore, we performed network analyses (MoDentify package) to identify sets of correlating metabolites that are coordinately regulated by the exposures.

RESULTS

We detected 2449 serum features of which 277 were annotated. After stringent analysis, 15 metabolites associated with at least one exposure (BMI, leptin, glucose, C-peptide, ISHOMA). Among these, palmitoleoyl ethanolamine (POEA), an endocannabinoid-like lipid endogenously synthesized from palmitoleic acid, and N-acetyl-L-alanine were consistently associated with C-peptide in all the analyses (95% CI: 0.10-0.34; effect size: 21%; p < 0.001; 95% CI: 0.04-0.10; effect size: 7%; p < 0.001). In network analysis, most features correlating with palmitoleoyl ethanolamide and N-acetyl-L-alanine and associated with C-peptide, were amino acids or dipeptides (n = 9, 35%), followed by lipids (n = 7, 27%).

CONCLUSIONS

We conclude that the metabolome of pregnant women with overweight/obesity is already altered early in pregnancy because of associated changes of C-peptide. Changes of palmitoleoyl ethanolamide concentration in pregnant women with obesity-associated hyperinsulinemia may reflect dysfunctional endocannabinoid-like signalling.

An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers

BACKGROUND

Gestational diabetes mellitus (GDM) represents the main metabolic alteration during pregnancy. The available methods for diagnosing GDM identify women when the disease is established, and pancreatic beta-cell insufficiency has occurred.The present study aimed to generate an early prediction model (under 18 weeks of gestation) to identify those women who will later be diagnosed with GDM.

METHODS

A cohort of 75 pregnant women was followed during gestation, of which 62 underwent normal term pregnancy and 13 were diagnosed with GDM. Targeted metabolomics was used to select serum biomarkers with predictive power to identify women who will later be diagnosed with GDM.

RESULTS

Candidate metabolites were selected to generate an early identification model employing a criterion used when performing Random Forest decision tree analysis. A model composed of two short-chain acylcarnitines was generated: isovalerylcarnitine (C5) and tiglylcarnitine (C5:1). An analysis by ROC curves was performed to determine the classification performance of the acylcarnitines identified in the study, obtaining an area under the curve (AUC) of 0.934 (0.873-0.995, 95% CI). The model correctly classified all cases with GDM, while it misclassified ten controls as in the GDM group. An analysis was also carried out to establish the concentrations of the acylcarnitines for the identification of the GDM group, obtaining concentrations of C5 in a range of 0.015-0.25 μmol/L and of C5:1 with a range of 0.015-0.19 μmol/L.

CONCLUSION

Early pregnancy maternal metabolites can be used to screen and identify pregnant women who will later develop GDM. Regardless of their gestational body mass index, lipid metabolism is impaired even in the early stages of pregnancy in women who develop GDM.

Prediction of pre-diabetes and type 2 diabetes nine years postpartum using serum metabolome in pregnant women with gestational diabetes requiring pharmacological treatment

AIMS

We examined the association between serum metabolome in women with pharmacologically treated gestational diabetes (GDM) and measures of glucose metabolism 9 years postpartum.

METHODS

Serum targeted metabolome, adiponectin, inflammatory markers, and insulin-like growth factor-binding protein-1 phosphoisoforms were analyzed at the time of diagnosing GDM. Glucose metabolism and insulin resistance were assessed at 9 years postpartum. Data from 119 subjects were available for analyses. Associations between baseline measures and future measures of glycemia were examined with univariate regressions and multivariate prediction models. This is a secondary analysis of a previous prospective trial (NCT02417090).

RESULTS

Baseline serum markers were most strongly related to measures of insulin resistance at 9-years follow-up. In multivariate analyses combination of IDL cholesterol, early gestational weight gain and in oral glucose tolerance test fasting and 2-h glucose predicted development of disorders of glucose metabolism (pre-diabetes and/or type 2 diabetes) better than clinical predictors alone (ROC-AUC 0.75 vs. 0.65, p = 0.020).

CONCLUSIONS

Serum metabolome in pregnancy in women with GDM is related to future glucose metabolism and insulin resistance. Compared to clinical variables alone metabolome might result in better prediction of future disorders of glucose metabolism and could facilitate personalized risk stratification for postpartum interventions and follow-up.

Aberrations in the early pregnancy serum metabolic profile in women with prediabetes at two years postpartum

INTRODUCTION

Aberrations in circulating metabolites have been associated with diabetes and cardiovascular risk.

OBJECTIVES

To investigate if early and late pregnancy serum metabolomic profiles differ in women who develop prediabetes by two years postpartum compared to those who remain normoglycemic.

METHODS

An NMR metabolomics platform was used to measure 228 serum metabolite variables from women with pre-pregnancy overweight in early and late pregnancy. Co-abundant groups of metabolites were compared between the women who were (n = 40) or were not (n = 138) prediabetic at two years postpartum. Random Forests classifiers, based on the metabolic profiles, were used to predict the prediabetes status, and correlations of the metabolites to glycemic traits (fasting glucose and insulin, HOMA2-IR and HbA1c) and hsCRP at postpartum were evaluated.

RESULTS

Women with prediabetes had higher concentrations of small HDL particles, total lipids in small HDL, phospholipids in small HDL and free cholesterol in small HDL in early pregnancy (p = 0.029; adj with pre-pregnancy BMI p = 0.094). The small HDL related metabolites also correlated positively with markers of insulin resistance at postpartum. Similar associations were not detected for metabolites in late pregnancy. A Random Forests classifier based on serum metabolites and clinical variables in early pregnancy displayed an acceptable predictive power for the prediabetes status at postpartum (AUROC 0.668).

CONCLUSION

Elevated serum concentrations of small HDL particles in early pregnancy associate with prediabetes and insulin resistance at two years postpartum. The serum metabolic profile during pregnancy might be used to identify women at increased risk for type 2 diabetes.

Pregnancy as an opportunity to prevent type 2 diabetes mellitus: FIGO Best Practice Advice

Gestational diabetes (GDM) impacts approximately 17 million pregnancies worldwide. Women with a history of GDM have an 8-10-fold higher risk of developing type 2 diabetes and a 2-fold higher risk of developing cardiovascular disease (CVD) compared with women without prior GDM. Although it is possible to prevent and/or delay progression of GDM to type 2 diabetes, this is not widely undertaken. Considering the increasing global rates of type 2 diabetes and CVD in women, it is essential to utilize pregnancy as an opportunity to identify women at risk and initiate preventive intervention. This article reviews existing clinical guidelines for postpartum identification and management of women with previous GDM and identifies key recommendations for the prevention and/or delayed progression to type 2 diabetes for global clinical practice.

Insufficient sleep disrupts glucose metabolism during pregnancy by inhibiting PGC-1α

Background

Gestational diabetes mellitus (GDM) impacted about 17 million pregnancies globally and predisposes both the mother and her offspring to metabolic disorders. Insufficient sleep has been shown to be associated with GDM. This study aimed to explore the molecular link between sleep and GDM.

Methods

The sleep of pregnant mice was disturbed with motion a rod and the mice received either dimethyl sulfoxide (DMSO) or ZLN005. Insulin resistance was assessed by intraperitoneal glucose tolerance test (GTT). Adenosine triphosphate (ATP), reactive oxygen species (ROS), and cytokines were measured with respective commercial kits. Gene expression was analyzed with quantitative polymerase chain reaction (qPCR), western blot, and/or immunohistochemistry (IHC).

Results

Sleep disturbance increased blood glucose level and insulin resistance, increased ROS and inflammatory cytokines, and reduced ATP level in pregnant mice. The expression levels of PGC-1α and downstream metabolic genes and antioxidant genes in pregnant mouse muscle were inhibited by sleep disturbance. ZLN005 promoted expression of PGC-1α and its target genes, increased muscle ATP level, decreased muscle ROS, and reduced blood glucose level and insulin resistance in sleep disturbed pregnant mice, indicating that PGC-1α played a critical role in sleep insufficiency caused GDM and might be a target for intervention.

Conclusions

PGC-1 was a key player in the sleep disorder GDM and might be a target for treatment.

Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies

The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal–offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant–metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal–offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal–offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.

Lower ATG7 Levels are Associated with a Higher Risk of Gestational Diabetes Mellitus: A Cross-Sectional Study

OBJECTIVE

This study aimed to investigate ATG7 levels in pregnant women with and without gestational diabetes mellitus (GDM) and explore the potential associations between ATG7 levels and GDM.

METHODS

This was a cross-sectional study conducted in a large tertiary hospital in Chengdu, China. The ATG7 levels in pregnant women at between 24 and 28 weeks of gestation with (n=84) and without GDM (n=649) were measured by using an ELISA kit. Glucose, HbA1c, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels were measured by an automatic biochemistry analyser. The homeostasis model assessment of insulin resistance (HOMA-IR) and insulin secretion (HOMA-β) were calculated according to published formulas. The associations of ATG7 levels with laboratory parameters, GDM, and insulin resistance were evaluated using correlation analysis or a regression model.

RESULTS

The ATG7 levels were significantly lower in pregnant women with GDM than in those without GDM. The correlation analyses found that ATG7 levels correlated positively with HOMA-β but correlated negatively with HOMA-IR, oral glucose tolerance test (OGTT) glucose levels, TGs, and LDL-C. There were no significant correlations between ATG7 levels and HbA1c, HDL-C, or TC. After adjusting for potential confounders, lower ATG7 levels were shown to be associated with a higher risk of GDM. Furthermore, multiple linear regression analyses showed that ATG7 levels were negatively associated with insulin resistance.

CONCLUSION

ATG7 levels are significantly lower in pregnant women with GDM than in those without GDM, and lower ATG7 levels are associated with a higher risk of GDM. ATG7 levels were negatively associated with insulin resistance. Autophagy deficiency, which is caused by lower ATG7 levels, may be the underlying mechanism that mediates insulin resistance in the development of GDM.

Serum and Amniotic Fluid Metabolic Profile Changes in Response to Gestational Diabetes Mellitus and the Association with Maternal-Fetal Outcomes

This study was designed to identify serum and amniotic fluid (AF) metabolic profile changes in response to gestational diabetes mellitus (GDM) and explore the association with maternal-fetal outcomes. We established the GDM rat models by combining a high-fat diet (HFD) with an injection of low-dose streptozotocin (STZ), detected the fasting plasma glucose (FPG) of pregnant rats in the second and third trimester, and collected AF and fetal rats by cesarean section on gestational day 19 (GD19), as well as measuring the weight and crown-rump length (CRL) of fetal rats. We applied liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the untargeted metabolomics analyses of serum and AF samples and then explored their correlation with maternal-fetal outcomes via the co-occurrence network. The results showed that 91 and 68 metabolites were upregulated and 125 and 78 metabolites were downregulated in serum and AF samples exposed to GDM, respectively. In maternal serum, the obvious alterations emerged in lipids and lipid-like molecules, while there were great changes in carbohydrate and carbohydrate conjugates, followed by amino acids, peptides, and analogs in amniotic fluid. The altered pathways both in serum and AF samples were amino acid, lipid, nucleotide, and vitamin metabolism pathways. In response to GDM, changes in the steroid hormone metabolic pathway occurred in serum, and an altered carbohydrate metabolism pathway was found in AF samples. Among differential metabolites in two kinds of samples, there were 34 common biochemicals shared by serum and AF samples, and a mutual significant association existed. These shared differential metabolites were implicated in several metabolism pathways, including choline, tryptophan, histidine, and nicotinate and nicotinamide metabolism, and among them, N1-methyl-4-pyridone-3-carboxamide, 5'-methylthioadenosine, and kynurenic acid were significantly associated with both maternal FPG and fetal growth. In conclusion, serum and AF metabolic profiles were remarkably altered in response to GDM. N1-Methyl-4-pyridone-3-carboxamide, 5'-methylthioadenosine, and kynurenic acid have the potential to be taken as biomarkers for maternal-fetal health status of GDM. The common and inter-related differential metabolites both in the serum and AF implied the feasibility of predicting fetal health outcomes via detecting the metabolites in maternal serum exposed to GDM.