Metabolomic profiling of women with gestational diabetes mellitus and their offspring: Review of metabolomics studies

First-trimester nuclear magnetic resonance-based metabolomic profiling increases the prediction of gestational diabetes mellitus

BACKGROUND

Current strategies for predicting gestational diabetes mellitus demonstrate suboptimal performance.

OBJECTIVE

To investigate whether nuclear magnetic resonance-based metabolomic profiling of maternal blood can be used for first-trimester prediction of gestational diabetes mellitus.

STUDY DESIGN

This was a prospective study of 20,000 women attending routine pregnancy care visits at 11 to 13 weeks' gestation. Metabolic profiles were assessed using a high-throughput nuclear magnetic resonance metabolomics platform. To inform translational applications, we focused on a panel of 34 clinically validated biomarkers for detailed analysis and risk modeling. All biomarkers were used to generate a multivariable logistic regression model to predict gestational diabetes mellitus. Data were split using a random seed into a 70% training set and a 30% validation set. Performance of the multivariable models was measured by receiver operating characteristic curve analysis and detection rates at fixed 10% and 20% false positive rates. Calibration for the combined risk model for all gestational diabetes mellitus was assessed visually through a figure showing the observed incidence against the predicted risk for gestational diabetes mellitus. A sensitivity analysis was conducted excluding the 64 women in our cohort who were diagnosed with gestational diabetes mellitus before 20 weeks' gestation.

RESULTS

The concentrations of several metabolomic biomarkers, including cholesterol, triglycerides, fatty acids, and amino acids, differed between women who developed gestational diabetes mellitus and those who did not. Addition of biomarker profile improved the prediction of gestational diabetes mellitus provided by maternal demographic characteristics and elements of medical history alone (before addition: area under the receiver operating characteristic curve, 0.790; detection rate, 50% [95% confidence interval, 44.3%-55.7%] at 10% false positive rate; and detection rate, 63% [95% confidence interval, 57.4%-68.3%] at 20% false positive rate; after addition: 0.840; 56% [50.3%-61.6%]; and 73% [67.7%-77.8%]; respectively). The performance of combined testing was better for gestational diabetes mellitus treated by insulin (area under the receiver operating characteristic curve, 0.905; detection rate, 76% [95% confidence interval, 67.5%-83.2%] at 10% false positive rate; and detection rate, 85% [95% confidence interval, 77.4%-90.9%] at 20% false positive rate) than gestational diabetes mellitus treated by diet alone (area under the receiver operating characteristic curve, 0.762; detection rate, 47% [95% confidence interval, 37.7%-56.5%] at 10% false positive rate; and detection rate, 64% [95% confidence interval, 54.5%-72.7%] at 20% false positive rate). The calibration plot showed good agreement between the observed incidence of gestational diabetes mellitus and the incidence predicted by the combined risk model. In the sensitivity analysis excluding the women diagnosed with gestational diabetes mellitus before 20 weeks' gestation, there was a negligible difference in the area under the receiver operating characteristic curve compared with the results from the entire cohort combined.

CONCLUSION

Addition of nuclear magnetic resonance-based metabolomic profiling to risk factors can provide first-trimester prediction of gestational diabetes mellitus.

Metabolomic Alterations Associated with Phthalate Exposures among Pregnant Women in Puerto Rico

Although phthalate exposure has been linked with multiple adverse pregnancy outcomes, their underlying biological mechanisms are not fully understood. We examined associations between biomarkers of phthalate exposures and metabolic alterations using untargeted metabolomics in 99 pregnant women and 86 newborns [mean (SD) gestational age = 39.5 (1.5) weeks] in the PROTECT cohort. Maternal urinary phthalate metabolites were quantified using isotope dilution high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS), while metabolic profiles in maternal and cord blood plasma were characterized via reversed-phase LC-MS. Multivariable linear regression was used in metabolome-wide association studies (MWAS) to identify individual metabolic features associated with elevated phthalate levels, while clustering and correlation network analyses were used to discern the interconnectedness of biologically relevant features. In the MWAS adjusted for maternal age and prepregnancy BMI, we observed significant associations between specific phthalates, namely, di(2-ethylhexyl) phthalate (DEHP) and mono(3-carboxypropyl) phthalate (MCPP), and 34 maternal plasma metabolic features. These associations predominantly included upregulation of fatty acids, amino acids, purines, or their derivatives and downregulation of ceramides and sphingomyelins. In contrast, fewer significant associations were observed with metabolic features in cord blood. Correlation network analysis highlighted the overlap of features associated with phthalates and those identified as differentiating markers for preterm birth in a previous study. Overall, our findings underscore the complex impact of phthalate exposures on maternal and fetal metabolism, highlighting metabolomics as a tool for understanding associated biological processes. Future research should focus on expanding the sample size, exploring the effects of phthalate mixtures, and validating identified metabolic features in larger, more diverse populations.

Unraveling diabetes complexity through natural products, miRNAs modulation, and future paradigms in precision medicine and global health

BACKGROUND AND AIMS

The challenge posed by diabetes necessitates a paradigm shift from conventional diagnostic approaches focusing on glucose and lipid levels to the transformative realm of precision medicine. This approach, leveraging advancements in genomics and proteomics, acknowledges the individualistic genetic variations, dietary preferences, and environmental exposures in diabetes management. The study comprehensively analyzes the evolving diabetes landscape, emphasizing the pivotal role of genomics, proteomics, microRNAs (miRNAs), metabolomics, and bioinformatics.

RESULTS

Precision medicine revolutionizes diabetes research and treatment by diverging from traditional diagnostic methods, recognizing the heterogeneous nature of the condition. MiRNAs, crucial post-transcriptional gene regulators, emerge as promising therapeutic targets, influencing key facets such as insulin signaling and glucose homeostasis. Metabolomics, an integral component of omics sciences, contributes significantly to diabetes research, elucidating metabolic disruptions, and offering potential biomarkers for early diagnosis and personalized therapies. Bioinformatics unveils dynamic connections between natural substances, miRNAs, and cellular pathways, aiding in the exploration of the intricate molecular terrain in diabetes. The study underscores the imperative for experimental validation in natural product-based diabetes therapy, emphasizing the need for in vitro and in vivo studies leading to clinical trials for assessing effectiveness, safety, and tolerability in real-world applications. Global cooperation and ethical considerations play a pivotal role in addressing diabetes challenges worldwide, necessitating a multifaceted approach that integrates traditional knowledge, cultural competence, and environmental awareness.

CONCLUSIONS

The key components of diabetes treatment, including precision medicine, metabolomics, bioinformatics, and experimental validation, converge in future strategies, embodying a holistic paradigm for diabetes care anchored in cutting-edge research and global healthcare accessibility.

Gestational Interrelationships among Gut-Metabolism-Transcriptome in Regulating Early Embryo Implantation and Placental Development in Mice

Decidualization of the uterine endometrium is a critical process for embryo implantation in mammals, primarily occurring on gestational day 8 in pregnant mice. However, the interplay between the maternal gut microbiome, metabolism, and the uterus at this specific time point remains poorly understood. This study employed a multi-omics approach to investigate the metabolic, gut microbiome, and transcriptomic changes associated with early pregnancy (gestational day 8 (E8)) in mice. Serum metabolomics revealed a distinct metabolic profile at E8 compared to controls, with the differential metabolites primarily enriched in amino acid metabolism pathways. The gut microbial composition showed that E8 mice exhibited higher alpha-diversity and a significant shift in beta-diversity. Specifically, the E8 group displayed a decrease in pathogenic Proteobacteria and an increase in beneficial Bacteroidetes and S24-7 taxa. Transcriptomics identified myriads of distinct genes between the E8 and control mice. The differentially expressed genes were enriched in pathways involved in alanine, aspartate, and glutamate metabolism, PI3K-Akt signaling, and the PPAR signaling pathway. Integrative analysis of the multi-omics data uncovered potential mechanistic relationships among the differential metabolites, gut microbiota, and uterine gene expression changes. Notably, the gene Asns showed strong correlations with specific gut S24-7 and metabolite L-Aspartatic acid, suggesting its potential role in mediating the crosstalk between the maternal environment and embryo development during early pregnancy. These findings provide valuable insights into the complex interplay between the maternal metabolome, the gut microbiome, and the uterine transcriptome in the context of early pregnancy, which may contribute to our understanding of the underlying mechanisms of embryo implantation and development.

Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study

INTRODUCTION

Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these.

OBJECTIVE

To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD.

METHODS

We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling.

RESULTS

499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides.

CONCLUSIONS

Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

Gestational testosterone excess early to mid-pregnancy disrupts maternal lipid homeostasis and activates biosynthesis of phosphoinositides and phosphatidylethanolamines in sheep

Gestational hyperandrogenism is a risk factor for adverse maternal and offspring outcomes with effects likely mediated in part via disruptions in maternal lipid homeostasis. Using a translationally relevant sheep model of gestational testosterone (T) excess that manifests maternal hyperinsulinemia, intrauterine growth restriction (IUGR), and adverse offspring cardiometabolic outcomes, we tested if gestational T excess disrupts maternal lipidome. Dimensionality reduction models following shotgun lipidomics of gestational day 127.1 ± 5.3 (term 147 days) plasma revealed clear differences between control and T-treated sheep. Lipid signatures of gestational T-treated sheep included higher phosphoinositides (PI 36:2, 39:4) and lower acylcarnitines (CAR 16:0, 18:0, 18:1), phosphatidylcholines (PC 38:4, 40:5) and fatty acids (linoleic, arachidonic, Oleic). Gestational T excess activated phosphatidylethanolamines (PE) and PI biosynthesis. The reduction in key fatty acids may underlie IUGR and activated PI for the maternal hyperinsulinemia evidenced in this model. Maternal circulatory lipids contributing to adverse cardiometabolic outcomes are modifiable by dietary interventions.

A Healthy, Low-Carbohydrate Diet During Pregnancy Is Associated With a Reduced Risk of Gestational Diabetes Mellitus

CONTEXT

Evidence on the associations of low-carbohydrate diet (LCD) during pregnancy with gestational diabetes mellitus (GDM) has been limited and inconsistent.

OBJECTIVE

We aimed to prospectively evaluate the risk of GDM associated with the LCD considering the quality of macronutrients.

METHODS

All participants were from a prospective cohort in Wuhan, China. The overall, healthy LCD (emphasizing low-quality carbohydrates, plant protein, and unsaturated fat), and unhealthy LCD (emphasizing high-quality carbohydrates, animal protein, and saturated fat) scores were calculated according to the percentage of energy intake from carbohydrates, protein, and fat. GDM was screened by a 75-g oral glucose tolerance test between 24 and 28 weeks. Poisson regression models were used to calculate relative risks (RRs) and 95% CIs.

RESULTS

Of 2337 pregnant women, 257 (11.0%) were diagnosed with GDM. Overall LCD score was not associated with risk of GDM, but the healthy and unhealthy LCD scores were associated with the risk of GDM. The multivariable-adjusted RRs (95% CI) were 0.68 (0.49-0.94) and 1.52 (1.11-2.08) for healthy and unhealthy LCD scores comparing the highest with the lowest quartile. Substituting high-quality carbohydrates for low-quality carbohydrates and animal protein, and substituting unsaturated fat for saturated fat, were associated with a 13% to 29% lower risk of GDM.

CONCLUSION

A healthy LCD during pregnancy characterized by high-quality carbohydrates, plant protein, and unsaturated fat was associated with a lower risk of GDM, whereas an unhealthy LCD consisting of low-quality carbohydrates, animal protein, and saturated fat was associated with a higher risk of GDM.

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.

Ceramides during Pregnancy and Obstetrical Adverse Outcomes

Ceramides are a group of sphingolipids located in the external plasma membrane layer and act as messengers in cellular pathways such as inflammatory processes and apoptosis. Plasma ceramides are biomarkers of cardiovascular disease, type 2 diabetes mellitus, Alzheimer's disease, various autoimmune conditions and cancer. During pregnancy, ceramides play an important role as stress mediators, especially during implantation, delivery and lactation. Based on the current literature, plasma ceramides could be potential biomarkers of obstetrical adverse outcomes, although their role in metabolic pathways under such conditions remains unclear. This review aims to present current studies that examine the role of ceramides during pregnancy and obstetrical adverse outcomes, such as pre-eclampsia, gestational diabetes mellitus and other complications.

Early ascending growth is associated with maternal lipoprotein profile during mid and late pregnancy and in cord blood

INTRODUCTION

Intrauterine conditions and accelerating early growth are associated with childhood obesity. It is unknown, whether fetal programming affects the early growth and could alterations in the maternal-fetal metabolome be the mediating mechanism. Therefore, we aimed to assess the associations between maternal and cord blood metabolite profile and offspring early growth.

METHODS

The RADIEL study recruited 724 women at high risk for gestational diabetes mellitus (GDM) BMI ≥ 30 kg/m2 and/or prior GDM) before or in early pregnancy. Blood samples were collected once in each trimester, and from cord. Metabolomics were analyzed by targeted nuclear magnetic resonance (NMR) technique. Following up on offsprings' first 2 years growth, we discovered 3 distinct growth profiles (ascending n = 80, intermediate n = 346, and descending n = 146) by using latent class mixed models (lcmm).

RESULTS

From the cohort of mother-child dyads with available growth profile data (n = 572), we have metabolomic data from 232 mothers from 1st trimester, 271 from 2nd trimester, 277 from 3rd trimester and 345 from cord blood. We have data on 220 metabolites in each trimester and 70 from cord blood. In each trimester of pregnancy, the mothers of the ascending group showed higher levels of VLDL and LDL particles, and lower levels of HDL particles (p < 0.05). When adjusted for gestational age, birth weight, sex, delivery mode, and maternal smoking, there was an association with ascending profile and 2nd trimester total cholesterol in HDL2, 3rd trimester total cholesterol in HDL2 and in HDL, VLDL size and ratio of triglycerides to phosphoglycerides (TG/PG ratio) in cord blood (p ≤ 0.002).

CONCLUSION

Ascending early growth was associated with lower maternal total cholesterol in HDL in 2nd and 3rd trimester, and higher VLDL size and more adverse TG/PG ratio in cord blood.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, http://www.

CLINICALTRIALS

com , NCT01698385.

An unwelcome inheritance: childhood obesity after diabetes in pregnancy

Diabetes in pregnancy affects 20 million women per year and is associated with increased risk of obesity in offspring, leading to insulin resistance and cardiometabolic disease. Despite the substantial public health ramifications, relatively little is known about the pathophysiological mechanisms underlying obesity in these high-risk children, which creates a barrier to successful intervention. While maternal glucose itself is undeniably a major stimulus upon intrauterine growth, the degree of offspring hyperinsulinism and disturbed lipid metabolism in mothers and offspring are also likely to be implicated in the disease process. The aim of this review is to summarise current understanding of the pathophysiology of childhood obesity after intrauterine exposure to maternal hyperglycaemia and to highlight possible opportunities for intervention. I present here a new unified hypothesis for the pathophysiology of childhood obesity in infants born to mothers with diabetes, which involves self-perpetuating twin cycles of pancreatic beta cell hyperfunction and altered lipid metabolism, both acutely and chronically upregulated by intrauterine exposure to maternal hyperglycaemia.

Midwives' and Diabetes Nurses' Experience of Screening and Care of Women with Gestational Diabetes Mellitus: A Qualitative Interview Study

Background

Gestational diabetes mellitus (GDM) is increasing and is associated with adverse outcomes for both mother and child. The metabolic demands of pregnancy can reveal a predisposition for type 2 diabetes mellitus (T2DM), and women with a history of GDM are more likely to develop T2DM than women with normoglycemic pregnancies.

Aim

The aim of this study was to explore midwives' and diabetes nurses' experience of their role in screening, care, and follow-up of women with gestational diabetes mellitus and, further, to explore their opinions and thoughts about existing routines and guidelines.

Method

Individual interviews were performed with ten diabetes nurses and eight midwives working in primary and special care. Qualitative content analysis was done according to Graneheim and Lundman.

Results

The analysis of the interviews resulted in the overall theme "An act of balance between normalcy and illness, working for motivation with dilemmas throughout the chain of health care." Difficulties in carrying out the important task of handling GDM while at the same time keeping the pregnancy in focus were central. Women were described as highly motivated to maintain a healthy lifestyle during pregnancy with the baby in mind, but it seemed difficult to maintain this after delivery, and compliance with long-term follow-up with the aim of reducing the risk of T2DM was low. The women came to the first follow-up but did not continue with later contact. This was at a time when the women felt healthy and were focusing on the baby and not themselves. A lack of cooperation and easy access to a dietician and physiotherapist were pointed out as well as a wish for resources such as group activities and multiprofessional teams.

Plasma Amino Acids in Early Pregnancy and Midpregnancy and Their Interplay With Phospholipid Fatty Acids in Association With the Risk of Gestational Diabetes Mellitus: Results From a Longitudinal Prospective Cohort

OBJECTIVE

We prospectively evaluated plasma amino acids (AAs) in early pregnancy and midpregnancy and their interplay with phospholipid fatty acids (FAs) in association with gestational diabetes mellitus (GDM) risk.

RESEARCH DESIGN AND METHODS

From a longitudinal pregnancy cohort of 2,802 individuals, concentrations of 24 plasma AAs at 10-14 and 15-26 gestational weeks (GW) were assessed among 107 GDM case subjects and 214 non-GDM control subjects. We estimated adjusted odds ratios (OR) and 95% CI for the associations of plasma AAs and the joint associations of plasma AAs and phospholipid FAs with GDM risk, adjusting for risk factors including age, prepregnancy BMI, and family history of diabetes.

RESULTS

Glycine at 10-14 GW was inversely associated with GDM (adjusted OR [95% CI] per SD increment: 0.55 [0.39-0.79]). Alanine, aspartic acid, and glutamic acid at 10-14 GW were positively associated with GDM (1.43 [1.08-1.88], 1.41 [1.11-1.80], and 1.39 [0.98-1.98]). At 15-26 GW, findings for glycine, alanine, aspartic acid, and the glutamine-to-glutamic acid ratio were consistent with the directions observed at 10-14 GW. Isoleucine, phenylalanine, and tyrosine were positively associated with GDM (1.64 [1.19-2.27], 1.15 [0.87-1.53], and 1.56 [1.16-2.09]). All P values for linear trend were <0.05. Several AAs and phospholipid FAs were significantly and jointly associated with GDM. For instance, the lowest risk was observed among women with higher glycine and lower even-chain saturated FAs at 10-14 GW (adjusted OR [95% CI] 0.15 [0.06, 0.37]).

CONCLUSIONS

Plasma AAs may be implicated in GDM development starting in early pregnancy. Associations of AAs with GDM may be enhanced in the copresence of phospholipid FA profile.

Association of Maternal Metabolites and Metabolite Networks with Newborn Outcomes in a Multi-Ancestry Cohort

The in utero environment is important for newborn size at birth, which is associated with childhood adiposity. We examined associations between maternal metabolite levels and newborn birthweight, sum of skinfolds (SSF), and cord C-peptide in a multinational and multi-ancestry cohort of 2337 mother-newborn dyads. Targeted and untargeted metabolomic assays were performed on fasting and 1 h maternal serum samples collected during an oral glucose tolerance test performed at 24-32 week gestation in women participating in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. Anthropometric measurements were obtained on newborns at birth. Following adjustment for maternal BMI and glucose, per-metabolite analyses demonstrated significant associations between maternal metabolite levels and birthweight, SSF, and cord C-peptide. In the fasting state, triglycerides were positively associated and several long-chain acylcarnitines were inversely associated with birthweight and SSF. At 1 h, additional metabolites including branched-chain amino acids, proline, and alanine were positively associated with newborn outcomes. Network analyses demonstrated distinct clusters of inter-connected metabolites significantly associated with newborn phenotypes. In conclusion, numerous maternal metabolites during pregnancy are significantly associated with newborn birthweight, SSF, and cord C-peptide independent of maternal BMI and glucose, suggesting that metabolites in addition to glucose contribute to newborn size at birth and adiposity.

Unique Metabolic Profiles Associate with Gestational Diabetes and Ethnicity in Low- and High-Risk Women Living in the UK

BACKGROUND

Gestational diabetes mellitus (GDM) is the most common global pregnancy complication; however, prevalence varies substantially between ethnicities, with South Asians (SAs) experiencing up to 3 times the risk of the disease compared with white Europeans (WEs). Factors driving this discrepancy are unclear, although the metabolome is of great interest as GDM is known to be characterized by metabolic dysregulation.

OBJECTIVES

The primary aim was to characterize and compare the metabolic profiles of GDM in SA and WE women (at <28 wk of gestation) from the Born in Bradford (BIB) prospective birth cohort in the United Kingdom.

METHODS

In total, 146 fasting serum metabolites, from 2,668 pregnant WE and 2,671 pregnant SA women (average BMI 26.2 kg/m2, average age 27.3 y) were analyzed using partial least squares discriminatory analyses to characterize GDM status. Linear associations between metabolite values and post-oral glucose tolerance test measures of dysglycemia (fasting glucose and 2 h postglucose) were also examined.

RESULTS

Seven metabolites associated with GDM status in both ethnicities (variable importance in projection ≥1), whereas 6 additional metabolites associated with GDM only in WE women. Unique metabolic profiles were observed in healthy-weight women who later developed GDM, with distinct metabolite patterns identified by ethnicity and BMI status. Of the metabolite values analyzed in relation to dysglycemia, lactate, histidine, apolipoprotein A1, HDL cholesterol, and HDL2 cholesterol associated with decreased glucose concentration, whereas DHA and the diameter of very low-density lipoprotein particles (nm) associated with increased glucose concertation in WE women, and in SAs, albumin alone associated with decreased glucose concentration.

CONCLUSIONS

This study shows that the metabolic risk profile for GDM differs between WE and SA women enrolled in BiB in the United Kingdom. This suggests that etiology of the disease differs between ethnic groups and that ethnic-appropriate prevention strategies may be beneficial.

Association between ambient air pollution exposure during pregnancy and gestational diabetes mellitus: a meta-analysis of cohort studies

Numerous studies have evaluated the association between air pollution and gestational diabetes mellitus (GDM), but the findings were inconsistent. This meta-analysis aimed to provide higher grade evidence on the association of air pollution with GDM based on previous studies. PubMed, Web of science, China National Knowledge Infrastructure (CNKI), and Wanfang Data Knowledge Service Platform (Wanfang) were searched comprehensively up to September 2021. Totally, 20 eligible cohort studies were finally included, for which the pooled RR and 95% CIs were estimated. Stratified analyses by study regions and units of pollutant increase were conducted for further investigation. Sensitivity analyses were also performed to assess the robustness. The finding showed that PM_2.5, PM₁₀, NO₂, and SO₂ exposure increased the risk of GDM, while O₃ exposure reduced GDM risk. Specifically, PM_2.5 exposure in the first and second trimesters, NO₂ and SO₂ exposure in the first trimester significantly increased the risk of GDM, with the RR ranging from 1.015 to 1.032. In addition, the elevation of GDM risk induced by PM_2.5, PM₁₀, and O₃ exposure was more pronounced in Asian subjects than in American subjects. The meta-analysis provides high-quality evidence on the effect of maternal air pollution exposure on GDM in each exposure period.

Molecular biomarkers for gestational diabetes mellitus and postpartum diabetes

ABSTRACT

Gestational diabetes mellitus (GDM) is a growing public health problem worldwide that threatens both maternal and fetal health. Identifying individuals at high risk for GDM and diabetes after GDM is particularly useful for early intervention and prevention of disease progression. In the last decades, a number of studies have used metabolomics, genomics, and proteomic approaches to investigate associations between biomolecules and GDM progression. These studies clearly demonstrate that various biomarkers reflect pathological changes in GDM. The established markers have potential use as screening and diagnostic tools in GDM and in postpartum diabetes research. In the present review, we summarize recent studies of metabolites, single-nucleotide polymorphisms, microRNAs, and proteins associated with GDM and its transition to postpartum diabetes, with a focus on their predictive value in screening and diagnosis.

The mediating role of maternal metabolites between lipids and adverse pregnancy outcomes of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, and the demographics of pregnant women have changed in recent decades. GDM is a metabolic disease with short- and long-term adverse effects on both pregnant women and newborns. The metabolic changes and corresponding risk factors should be of great significance in understanding the pathological mechanism of GDM and reducing the incidence of adverse pregnancy outcomes in patients with GDM. The well-known GDM-associated lipids used in clinical tests, such as triglyceride (TG), are thought to play a major role in metabolic changes during GDM, which have a potential causal relationship with abnormal pregnancy outcomes of GDM. Therefore, this study analyzed the relationship between clinical lipid indicators, metabolic profiles, and abnormal pregnancy outcomes in GDM through mediation analysis. By constructing a metabolic atlas of 399 samples from GDM patients in different trimesters, we efficiently detected the key metabolites of adverse pregnancy outcomes and their mediating roles in bridging abnormal lipids and adverse pregnancy outcomes in patients with GDM. Our study confirmed that TG and total cholesterol were independent risk factors for adverse pregnancy outcomes in patients with GDM. Several key metabolites as mediators (e.g., gamma-linolenic acid, heptadecanoic acid, oleic acid, palmitic acid, and palmitoleic acid) have been identified as potential biomarkers for adverse pregnancy outcomes in patients with GDM. These metabolites mainly participate in the biosynthesis of unsaturated fatty acids, which may shed new light on the pathology of GDM and provide insights for further exploration of the molecular mechanisms underlying adverse pregnancy outcomes.

Predictive Metabolomic Markers in Early to Mid-pregnancy for Gestational Diabetes Mellitus: A Prospective Test and Validation Study

Gestational diabetes mellitus (GDM) predisposes pregnant individuals to perinatal complications and long-term diabetes and cardiovascular diseases. We developed and validated metabolomic markers for GDM in a prospective test-validation study. In a case-control sample within the PETALS cohort (GDM n = 91 and non-GDM n = 180; discovery set), a random PETALS subsample (GDM n = 42 and non-GDM n = 372; validation set 1), and a case-control sample within the GLOW trial (GDM n = 35 and non-GDM n = 70; validation set 2), fasting serum untargeted metabolomics were measured by gas chromatography/time-of-flight mass spectrometry. Multivariate enrichment analysis examined associations between metabolites and GDM. Ten-fold cross-validated LASSO regression identified predictive metabolomic markers at gestational weeks (GW) 10-13 and 16-19 for GDM. Purinone metabolites at GW 10-13 and 16-19 and amino acids, amino alcohols, hexoses, indoles, and pyrimidine metabolites at GW 16-19 were positively associated with GDM risk (false discovery rate <0.05). A 17-metabolite panel at GW 10-13 outperformed the model using conventional risk factors, including fasting glycemia (area under the curve: discovery 0.871 vs. 0.742, validation 1 0.869 vs. 0.731, and validation 2 0.972 vs. 0.742; P < 0.01). Similar results were observed with a 13-metabolite panel at GW 17-19. Dysmetabolism is present early in pregnancy among individuals progressing to GDM. Multimetabolite panels in early pregnancy can predict GDM risk beyond conventional risk factors.

Maternal and Fetal Metabolites in Gestational Diabetes Mellitus: A Narrative Review

Gestational diabetes mellitus (GDM) is a major public health issue of our century due to its increasing prevalence, affecting 5% to 20% of all pregnancies. The pathogenesis of GDM has not been completely elucidated to date. Increasing evidence suggests the association of environmental factors with genetic and epigenetic factors in the development of GDM. So far, several metabolomics studies have investigated metabolic disruptions associated with GDM. The aim of this review is to highlight the usefulness of maternal metabolites as diagnosis markers of GDM as well as the importance of both maternal and fetal metabolites as prognosis biomarkers for GDM and GDM’s transition to type 2 diabetes mellitus T2DM.

Biomolecular characterization of placental tissues in gestational diabetes mellitus using Fourier transform infrared microspectroscopy

Gestational diabetes mellitus (GDM) is a common complication during pregnancy. It could cause severe side-effect on the mother's and newborn's heath in the short- and long-term. Prevalence has been increasing over time, likely due to increases in mean maternal age and body weight. However, how GDM affects the placenta structure and function are still unclear. Fourier transform infrared microspectroscopy is well suited to study biological samples, such as tissues and cells. Biomolecules of human tissues have characteristic absorptions in mid-infrared range. In this study, Fourier transform infrared microspectroscopy was used to measure unfixed placental tissue sections from women with GDM and matched controls. The molecular composition of different type of placental tissue sections were further analyzed with principle component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). The major spectral characteristic of biomolecules in GDM placental tissue and control group were compared. The conformational change of lipid chains and higher level of lipid oxidation were found for placental tissues from GDM pregnancies. The increase of proteins β-sheet structures relative to the α-helix structures in the GDM placental tissues were also found. The fingerprint region showed the variances of carbohydrates, nucleic acids and phospholipids between GDM and control group placental tissues. These findings are helpful for understanding how GDM affects placenta's biochemical composition and how GDM causes maternal and fetal metabolism changes. This study also provides a new approach to investigating biomolecular composition of samples from GDM pregnancy through spectroscopic method.

The birth of cardiac disease: Mechanisms linking gestational diabetes mellitus and early onset of cardiovascular disease in offspring

Cardiovascular disease (CVD) is the biggest killer worldwide, composing a major economic burden for health care systems. Obesity and diabetes are dual epidemics on the rise and major risk factors predisposing for CVD. Increased obesity- and diabetes-related incidence is now observed among children, adolescents, and young adults. Gestational diabetes mellitus (GDM) is the most common metabolic pregnancy disorder, and its prevalence is rapidly increasing. During pregnancies complicated by GDM, the offspring are exposed to a compromised intrauterine environment characterized by hyperglycemic periods. Unfavorable in utero conditions at critical periods of fetal cardiac development can produce developmental adaptations that remodel the cardiovascular system in a way that can contribute to adult-onset of heart disease due to the programming during fetal life. Epidemiological studies have reported increased cardiovascular complications among GDM-descendants, highlighting the urgent need to investigate and understand the mechanisms modulated during fetal development of in utero GDM-exposed offspring that predispose an individual to increased CVD during life. In this manuscript, we overview previous studies in this area and gather evidence linking GDM and CVD development in the offspring, providing new insights on novel mechanisms contributing to offspring CVD programming by GDM, from the role of maternal-fetal interactions to their impact on fetal cardiovascular development, how the perpetuation of cardiac programming is maintained in postnatal life, and advance the intergenerational implications contributing to increased CVD premature origin. Understanding the perpetuation of CVD can be the first step to manage and reverse this leading cause of morbidity and mortality. This article is categorized under: Reproductive System Diseases > Molecular and Cellular Physiology Cardiovascular Diseases > Molecular and Cellular Physiology Metabolic Diseases > Genetics/Genomics/Epigenetics.

A Prospective Study of Early-pregnancy Thyroid Markers, Lipid Species, and Risk of Gestational Diabetes Mellitus

CONTEXT

While the associations between thyroid markers and gestational diabetes mellitus (GDM) have been extensively studied, the results are inconclusive and the mechanisms remain unclear.

OBJECTIVE

We aimed to investigate the prospective associations of thyroid markers in early gestation with GDM risk, and examine the mediating effects through lipid species.

METHODS

This study included 6068 pregnant women from the Tongji-Shuangliu Birth Cohort. Maternal serum thyroid markers (free triiodothyronine (fT3), free thyroxine (fT4), thyroid-stimulating hormone, thyroid peroxidase antibody, and thyroglobulin antibody) were measured before 15 weeks. Deiodinase activity was assessed by fT3/fT4 ratio. Plasma lipidome were quantified in a subset of 883 participants.

RESULTS

Mean age of the participants was 26.6 ± 3.7 years, and mean gestational age was 10.3 ± 2.0 weeks. Higher levels of fT4 were associated with a decreased risk of GDM (OR = 0.73 comparing the extreme quartiles; 95% CI 0.54, 0.98, Ptrend = .043), while higher fT3/fT4 ratio was associated with an increased risk of GDM (OR = 1.43 comparing the extreme quartiles; 95% CI 1.06, 1.93, Ptrend = .010) after adjusting for potential confounders. Multiple linear regression suggested that fT3/fT4 ratio was positively associated with alkylphosphatidylcholine 36:1, phosphatidylethanolamine plasmalogen 38:6, diacylglyceride 18:0/18:1, sphingomyelin 34:1, and phosphatidylcholine 40:7 (false discovery rate [FDR] adjusted P < .05). Mediation analysis indicated 67.9% of the association between fT3/fT4 ratio and GDM might be mediated through the composite effect of these lipids.

CONCLUSION

Lower concentration of serum fT4 or higher fT3/fT4 ratio in early pregnancy was associated with an increased risk of GDM. The association of fT3/fT4 ratio with GDM was largely mediated by specific lipid species.

Molecular Mechanism of miR-29b on Gestational Diabetes and Its Influence on Trophoblast Cell Function

To explore the mechanism of miR-29b in gestational diabetes mellitus (GDM) and its effect on the function of trophoblast cell (TBC), the placenta tissues of 55 normal term pregnancies and 55 GDM patients were selected and rolled into control group and observation group. In the early stage, microRNA (miRNA) chips were utilized to screen the differentially expressed miRNAs in the placenta of observation group and control group. According to the microarray results of miRNAs, three differentially expressed miRNAs, namely let-7b, miR-1202, and miR-29b were selected. Then, the differences in the miR-29b level in the four groups were analyzed, namely the microRNA-29b (miR-29b minic), mini-control (minic control), microRNA-29b inhibitor (miR-29b inhibitor), and inhibitor control (inhibitor control). The results showed that miR-29b level in the placenta of observation group was substantially inferior to that of controls, with remarkable differences (P < 0.05). miR-29b level in miR-29b minic and minic control had significant changes (P < 0.01). The TBC activity of minic control was greatly superior to that of minic control, and there was considerable difference between the two (P < 0.05). The difference between miR-29b inhibitor and inhibitor control in TBC was not obvious, without considerable differences (P > 0.05). The invasion ability of miR-29b inhibitor TBC was notably superior to inhibitor control, and there were substantial differences (P < 0.05). To sum up, miR-29b had a significant inhibitory effect on the proliferation and cell activity of TBC, and can promote the apoptosis and death of TBC. Moreover, its inhibitory effect on cell migration and invasion was also suggested.

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.

Non-esterified fatty acids as biomarkers of diet and glucose homeostasis in pregnancy: The impact of fatty acid reporting methods: NEFA reporting methods affect dietary and cardiometabolic endpoints

BACKGROUND

Sparse data exists on the utility of individual serum non-esterified fatty acids (NEFAs) as clinical and dietary biomarkers and how reporting methods could affect these associations. We investigated the associations of 19 serum NEFAs expressed as µM or mol%, with self-reported dietary intake data, and cardiometabolic health indicators in pregnant women.

METHODS

In this cross-sectional study, 273 pregnant women in their second trimester each completed a semi-quantitative food-frequency questionnaire and provided fasting serum samples. Comprehensive serum NEFA analysis was performed by multisegment injection-nonaqueous capillary electrophoresis-mass spectrometry. We evaluated the associations of NEFAs using two different reporting methods, with diet quality, specific foods intake, and measures of adiposity and glucose homeostasis.

RESULTS

Consistently stronger dietary correlations were observed when expressed as mol%. Serum ω-3 NEFAs were associated with diet quality and fish/fish oil daily servings (DHA mol%, r= 0.37; p = 4.8e-10), and odd-chain NEFAs were associated with full-fat dairy intake (15:0 mol%, r = 0.23; p = 9.0e-5). Glucose intolerance was positively associated with odd chain NEFAs as expressed in µM (r = 0.21; p= 0.001) but inversely associated when expressed as mol% (r = -0.31; p= 2.2e-7). In contrast, monounsaturated NEFAs (µM and mol%) had robust positive associations with pre-pregnancy BMI, second trimester skin-fold thickness, glycated hemoglobin, fasting glucose, and glucose intolerance.

CONCLUSIONS

This study demonstrates the utility of specific NEFAs and their sub-classes as viable dietary and clinical biomarkers when reported as their relative proportions. More research is needed to investigate inconsistencies between absolute concentrations and relative proportions when reporting fatty acids.

Developmental Effects of (Pre-)Gestational Diabetes on Offspring: Systematic Screening Using Omics Approaches

Worldwide, gestational diabetes affects 2-25% of pregnancies. Due to related disturbances of the maternal metabolism during the periconceptional period and pregnancy, children bear an increased risk for future diseases. It is well known that an aberrant intrauterine environment caused by elevated maternal glucose levels is related to elevated risks for increased birth weights and metabolic disorders in later life, such as obesity or type 2 diabetes. The complexity of disturbances induced by maternal diabetes, with multiple underlying mechanisms, makes early diagnosis or prevention a challenging task. Omics technologies allowing holistic quantification of several classes of molecules from biological fluids, cells, or tissues are powerful tools to systematically investigate the effects of maternal diabetes on the offspring in an unbiased manner. Differentially abundant molecules or distinct molecular profiles may serve as diagnostic biomarkers, which may also support the development of preventive and therapeutic strategies. In this review, we summarize key findings from state-of-the-art Omics studies addressing the impact of maternal diabetes on offspring health.

Metabolites involved in purine degradation, insulin resistance, and fatty acid oxidation are associated with prediction of Gestational diabetes in plasma

INTRODUCTION

Gestational diabetes mellitus (GDM) significantly increases maternal and fetal health risks, but factors predictive of GDM are poorly understood.

OBJECTIVES

Plasma metabolomics analyses were conducted in early pregnancy to identify potential metabolites associated with prediction of GDM.

METHODS

Sixty-eight pregnant women with overweight/obesity from a clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 34; GDM group) were matched on treatment group, age, body mass index, and ethnicity with those who did not develop GDM (n = 34; Non-GDM group). Blood draws were completed early in pregnancy (10-16 weeks). Plasma samples were analyzed by UPLC-MS using three metabolomics assays.

RESULTS

One hundred thirty moieties were identified. Thirteen metabolites including pyrimidine/purine derivatives involved in uric acid metabolism, carboxylic acids, fatty acylcarnitines, and sphingomyelins (SM) were different when comparing the GDM vs. the Non-GDM groups (p < 0.05). The most significant differences were elevations in the metabolites' hypoxanthine, xanthine and alpha-hydroxybutyrate (p < 0.002, adjusted p < 0.02) in GDM patients. A panel consisting of four metabolites: SM 14:0, hypoxanthine, alpha-hydroxybutyrate, and xanthine presented the highest diagnostic accuracy with an AUC = 0.833 (95% CI: 0.572686-0.893946), classifying as a "very good panel".

CONCLUSION

Plasma metabolites mainly involved in purine degradation, insulin resistance, and fatty acid oxidation, were altered in early pregnancy in connection with subsequent GDM development.

Placental metabolic profiling in gestational diabetes mellitus: An important role of fatty acids

Aim

Gestational diabetes mellitus (GDM) is the most common metabolic disorder during pregnancy. Accumulating studies have reported metabolites that are significantly associated with the development of GDM. However, studies on the metabolism of placenta, the most important organ of maternal‐fetal energy and material transport, are extremely rare. This study aimed to identify and discuss the relationship between differentially expressed metabolites (DEM) and clinical parameters of the mothers and newborns.

Methods

In this study, metabolites from 63 placenta tissues (32 GDM and 31 normal controls) were assayed by ultra‐performance liquid chromatography‐high resolution mass spectrometry (UPLC‐HRMS).

Results

A total of 1297 annotated metabolites were detected, of which 87 significantly different in GDM placenta. Lipids and lipid‐like molecules accounted for 62.1% of DEM as they were significantly enriched via the “biosynthesis of unsaturated fatty acids” and “fatty acid biosynthesis” pathways. Linoleic acid and α‐linolenic acid appeared to be good biomarkers for the prediction and diagnosis of GDM. In addition, the level of PC(14:0/18:0) was negatively correlated with neonatal weight. 14 metabolites significantly different in male and female offspring, with the most increase in female newborns.

Conclusion

Even if maternal blood glucose level is well controlled, there are still metabolic abnormalities in GDM. Lipids and lipid‐like molecules were the main differential metabolites, especially unsaturated fatty acids.

Plasma lipidomics in early pregnancy and risk of gestational diabetes mellitus: a prospective nested case-control study in Chinese women

BACKGROUND

Lipid metabolism plays an important role in the pathogenesis of diabetes. There is little evidence regarding the prospective association of the maternal lipidome with gestational diabetes mellitus (GDM), especially in Chinese populations.

OBJECTIVES

We aimed to identify novel lipid species associated with GDM risk in Chinese women, and assess the incremental predictive capacity of the lipids for GDM.

METHODS

We conducted a nested case-control study using the Tongji-Shuangliu Birth Cohort with 336 GDM cases and 672 controls, 1:2 matched on age and week of gestation. Maternal blood samples were collected at 6-15 wk, and lipidomes were profiled by targeted ultra-HPLC-tandem MS. GDM was diagnosed by oral-glucose-tolerance test at 24-28 wk. The least absolute shrinkage and selection operator is a regression analysis method that was used to select novel biomarkers. Multivariable conditional logistic regression was used to estimate the associations.

RESULTS

Of 366 detected lipids, 10 were selected and found to be significantly associated with GDM independently of confounders: there were positive associations with phosphatidylinositol 40:6, alkylphosphatidylcholine 36:1, phosphatidylethanolamine plasmalogen 38:6, diacylglyceride 18:0/18:1, and alkylphosphatidylethanolamine 40:5 (adjusted ORs per 1 log-SD increment range: 1.34-2.86), whereas there were inverse associations with sphingomyelin 34:1, dihexosyl ceramide 24:0, mono hexosyl ceramide 18:0, dihexosyl ceramide 24:1, and phosphatidylcholine 40:7 (adjusted ORs range: 0.48-0.68). Addition of these novel lipids to the classical GDM prediction model resulted in a significant improvement in the C-statistic (discriminatory power of the model) to 0.801 (95% CI: 0.772, 0.829). For every 1-point increase in the lipid risk score of the 10 lipids, the OR of GDM was 1.66 (95% CI: 1.50, 1.85). Mediation analysis suggested the associations between specific lipid species and GDM were partially explained by glycemic and insulin-related indicators.

CONCLUSIONS

Specific plasma lipid biomarkers in early pregnancy were associated with GDM in Chinese women, and significantly improved the prediction for GDM.

Application of Metabolomics in Diagnosis of Cow Mastitis: A Review

Cow mastitis, with high incidence rate and complex cause of disease, is one of the main diseases that affect the development of dairy industry in the world. Clinical mastitis and subclinical mastitis caused by Staphylococcus aureus, Escherichia coli, Streptococcus, and other pathogens have a huge potential safety hazard to food safety and the rapid development of animal husbandry. The economic loss caused by cow mastitis is billions of dollars every year in the world. In recent years, the omics technology has been widely used in animal husbandry with the continuous breakthrough of sequencing technology and the continuous reduction of sequencing cost. For dairy cow mastitis, the traditional diagnostic technique, such as histopathological screening, somatic cell count, milk pH test, milk conductivity test, enzyme activity test, and infrared thermography, are difficult to fully and comprehensively clarify its pathogenesis due to their own limitations. Metabolomics technology is an important part of system biology, which can simultaneously analyze all low molecular weight metabolites such as amino acids, lipids, carbohydrates under the action of complex factors including internal and external environment and in a specific physiological period accurately and efficiently, and then clarify the related metabolic pathways. Metabolomics, as the most downstream of gene expression, can amplify the small changes of gene and protein expression at the level of metabolites, which can more fully reflect the cell function. The application of metabolomics technology in cow mastitis can analyze the hetero metabolites, identify the related biomarkers, and reveal the physiological and pathological changes of cow mammary gland, so as to provide valuable reference for the prediction, diagnosis, and treatment of mastitis. The research progress of metabolomics technology in cow mastitis in recent years was reviewed, in order to provide guidance for the development of cow health and dairy industry safety in this manuscript.

Longitudinal Plasma Metabolomics Profile in Pregnancy-A Study in an Ethnically Diverse U.S. Pregnancy Cohort

Amino acids, fatty acids, and acylcarnitine metabolites play a pivotal role in maternal and fetal health, but profiles of these metabolites over pregnancy are not completely established. We described longitudinal trajectories of targeted amino acids, fatty acids, and acylcarnitines in pregnancy. We quantified 102 metabolites and combinations (37 fatty acids, 37 amino acids, and 28 acylcarnitines) in plasma samples from pregnant women in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies—Singletons cohort (n = 214 women at 10–14 and 15–26 weeks, 107 at 26–31 weeks, and 103 at 33–39 weeks). We used linear mixed models to estimate metabolite trajectories and examined variation by body mass index (BMI), race/ethnicity, and fetal sex. After excluding largely undetected metabolites, we analyzed 77 metabolites and combinations. Levels of 13 of 15 acylcarnitines, 7 of 25 amino acids, and 18 of 37 fatty acids significantly declined over gestation, while 8 of 25 amino acids and 10 of 37 fatty acids significantly increased. Several trajectories appeared to differ by BMI, race/ethnicity, and fetal sex although no tests for interactions remained significant after multiple testing correction. Future studies merit longitudinal measurements to capture metabolite changes in pregnancy, and larger samples to examine modifying effects of maternal and fetal characteristics.

Associations of prenatal metabolomics profiles with early childhood growth trajectories and obesity risk in African Americans: the CANDLE study

OBJECTIVE

Prenatal metabolomics profiles, providing measures of in utero nutritional and environmental exposures, may improve the prediction of childhood outcomes. We aimed to identify prenatal plasma metabolites associated with early childhood body mass index (BMI) trajectories and overweight/obesity risk in offspring.

METHODS

This study included 450 African American mother-child pairs from the Conditions Affecting Neurocognitive Development and Learning in Early Childhood Study. An untargeted metabolomics analysis was performed on the mothers' plasma samples collected during the second trimester. The children's BMI-z-score trajectories from birth to age 4 [rising-high- (9.8%), moderate- (68.2%), and low-BMI (22.0%)] and overweight/obesity status at age 4 were the main outcomes. The least absolute shrinkage and selection operator (LASSO) was used to select the prenatal metabolites associated with childhood outcomes.

RESULTS

The mothers were 24.5 years old on average at recruitment, 76.4% having education less than 12 years and 80.0% with Medicaid or Medicare. In LASSO, seven and five prenatal metabolites were associated with the BMI-z-score trajectories and overweight/obese at age 4, respectively. These metabolites are mainly from/relevant to the pathways of steroid biosynthesis, amino acid metabolism, vitamin B complex, and xenobiotics metabolism (e.g., caffeine and nicotine). The odds ratios (95% CI) associated with a one SD increase in the prenatal metabolite risk scores (MRSs) constructed from the LASSO-selected metabolites were 2.97 (1.95-4.54) and 2.03 (1.54-2.67) for children being in the rising-high-BMI trajectory group and overweight/obesity at age 4, respectively. The MRSs significantly improved the risk prediction for childhood outcomes beyond traditional prenatal risk factors. The increase (95% CI) in the area under the receiver operating characteristic curves were 0.10 (0.03-0.18) and 0.07 (0.02-0.12) for the rising-high-BMI trajectory (P = 0.005) and overweight/obesity at age 4 (P = 0.007), respectively.

CONCLUSIONS

Prenatal metabolomics profiles advanced prediction of early childhood growth trajectories and obesity risk in offspring.

Distinct metabolite profiles of adiposity indices and their relationships with habitual diet in young adults

BACKGROUND AND AIMS

Obesity is characterized as overall or regional adiposity accumulation. However, the metabolic status underlying fat accumulation was not well understood. We sought to identify metabolite profiles based on their correlations with body mass index (BMI), body fat percentage (BFP), waist circumference (WC), and visceral adiposity index (VAI) in young Chinese adults (19-37 years old), and their associations with dietary consumption were also explored.

METHODS AND RESULTS

A total of 86 plasma samples were analyzed using untargeted lipidomics and metabolomics approaches. Metabolite profiles of adiposity indices were identified using random forest modelling. Ridge regression was used to generate metabolite scores. Overall, 30, 46, 30, and 20 metabolites correlated with BMI, BFP, WC, and VAI, respectively, which resulted in metabolite scores for each index. Top three enriched categories of the identified metabolites were glycerophospholipids, glycerolipids, and sphingolipids, with some specific metabolites (such as phosphatidylserine (37:2), phatidylethanolamine (42:4), and ceramide (40:0)) exclusively associated with overall adiposity, and some other metabolites exclusively associated with abdominal adiposity indices, e.g., triradylglycerol (45:0, 52:4, and 35:0) and diacylglycerol (38:4, 36:3, and 36:5). Moreover, metabolite scores were negatively associated with the intake of food rich in protein or fiber, while they were positively associated with food rich in carbohydrate, with similar results for adiposity indices.

CONCLUSION

We observed unique metabolite profiles of regional or overall fat deposition in young adults. Glycerophospholipids, glycerolipids, or sphingolipids may be involved in the regulation of adiposity accumulation, affected by dietary exposures.

The clinical impact of maternal weight on offspring health: lights and shadows in breast milk metabolome

INTRODUCTION

Pre-pregnancy overweight and obesity, depending on maternal nutrition and metabolic state, can influence fetal, neonatal and long-term offspring health, regarding cardio-metabolic, respiratory, immunological and cognitive outcomes. Thus, maternal weight can act, through mechanisms that are not full understood, on the physiology and metabolism of some fetal organs and tissues, to adapt themselves to the intrauterine environment and nutritional reserves. These effects could occur by modulating gene expression, neonatal microbiome, and through breastfeeding.

AREAS COVERED

In this paper, we investigated the potential effects of metabolites found altered in breast milk (BM) of overweight/obese mothers, through an extensive review of metabolomics studies, and the potential short- and long-term clinical effects in the offspring, especially regarding overweight, glucose homeostasis, insulin resistance, oxidative stress, infections, immune processes, and neurodevelopment.

EXPERT OPINION

Metabolomics seems the ideal tool to investigate BM variation depending on maternal or fetal/neonatal factors. In particular, BM metabolome alterations according to maternal conditions were recently pointed out in cases of gestational diabetes, preeclampsia, intrauterine growth restriction and maternal overweight/obesity. In our opinion, even if BM is the food of choice in neonatal nutrition, the deepest comprehension of its composition in overweight/obese mothers could allow targeted supplementation, to improve offspring health and metabolic homeostasis.

Metabolomic Biomarkers in Gestational Diabetes Mellitus: A Review of the Evidence

Gestational diabetes mellitus (GDM) is the fastest growing type of diabetes, affecting between 2 to 38% of pregnancies worldwide, varying considerably depending on diagnostic criteria used and sample population studied. Adverse obstetric outcomes include an increased risk of macrosomia, and higher rates of stillbirth, instrumental delivery, and birth trauma. Metabolomics, which is a platform used to analyse and characterise a large number of metabolites, is increasingly used to explore the pathophysiology of cardiometabolic conditions such as GDM. This review aims to summarise metabolomics studies in GDM (from inception to January 2021) in order to highlight prospective biomarkers for diagnosis, and to better understand the dysfunctional metabolic pathways underlying the condition. We found that the most commonly deranged pathways in GDM include amino acids (glutathione, alanine, valine, and serine), carbohydrates (2-hydroxybutyrate and 1,5-anhydroglucitol), and lipids (phosphatidylcholines and lysophosphatidylcholines). We also highlight the possibility of using certain metabolites as predictive markers for developing GDM, with the use of highly stratified modelling techniques. Limitations for metabolomic research are evaluated, and future directions for the field are suggested to aid in the integration of these findings into clinical practice.

Circular RNA expression profiles in umbilical cord blood exosomes from normal and gestational diabetes mellitus patients

Circular RNA (circRNA) is a novel member of endogenous noncoding RNAs with widespread distribution and diverse cellular functions. Recently, circRNAs have been identified for their enrichment and stability in exosomes. However, the roles of circRNAs from umbilical cord blood exosomes in gestational diabetes mellitus (GDM) occurrence and fetus growth remains poorly understood. In the present study, we used microarray technology to construct a comparative circRNA profiling of umbilical cord blood exosomes between GDM patients and controls. We found the exosome particle size was larger, and the exosome concentration was higher in the GDM patients. A total of 88,371 circRNAs in umbilical cord blood exosomes from two groups were evaluated. Of these, 229 circRNAs were significantly up-regulated and 278 circRNAs were significantly down-regulated in the GDM patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathway analyses demonstrated that circRNA parental genes involved in the regulation of metabolic process, growth and development were significantly enriched, which are important in GDM development and fetus growth. Further circRNA/miRNA interactions analysis showed that most of the exosomal circRNAs harbored miRNA binding sites, and some miRNAs were associated with GDM. Collectively, these results lay a foundation for extensive studies on the role of exosomal circRNAs in GDM development and fetus growth.

Metabolomic profile associated with obstructive sleep apnoea severity in obese pregnant women with gestational diabetes mellitus: A pilot study

Obstructive sleep apnoea (OSA) is prevalent in obese women with gestational diabetes mellitus (GDM). The present pilot study explored associations between OSA severity and metabolites in women with GDM. A total of 81 obese women with diet-controlled GDM had OSA assessment (median gestational age [GA] 29 weeks). The metabolic profile was assayed from fasting serum samples via liquid chromatography-mass spectrometry (LC-MS) using an untargeted approach. Metabolites were extracted and subjected to an Agilent 1,290 UPLC coupled to an Agilent 6,545 quadrupole time-of-flight (Q-TOF) MS. Data were acquired using electrospray ionisation in positive and negative ion modes. The raw LC-MS data were processed using the OpenMS toolkit to detect and quantify features, and these features were annotated using the Human Metabolite Database. The feature data were compared with OSA status, apnea-hypopnea index (AHI), body mass index (BMI) and GA using "limma" in R. Correlation analyses of the continuous covariates were performed using Kendall's Tau test. The p values were adjusted for multiple testing using the Benjamini-Hochberg false discovery rate correction. A total of 42 women (51.8%) had OSA, with a median AHI of 9.1 events/hr. There were no significant differences in metabolomics profiles between those with and without OSA. However, differential analyses modelling in GA and BMI found 12 features that significantly associated with the AHI. These features could be annotated to oestradiols, lysophospholipids, and fatty acids, with higher levels related to higher AHI. Metabolites including oestradiols and phospholipids may be involved in pathogenesis of OSA in pregnant women with GDM. A targeted approach may help elucidate our understanding of their role in OSA in this population.

Differentiation of gestational diabetes mellitus by nuclear magnetic resonance-based metabolic plasma analysis

This study aimed to investigate the metabolic profile of gestational diabetes mellitus (GDM) at both antepartum and postpartum periods. Seventy pregnant women were divided into three groups: the normal glucose-tolerant group (NGT, n=35), the abnormal glucose-tolerant groups without insulin therapy (A1GDM, n=24) or with insulin therapy (A2GDM, n=11). Metabolic profiles of the plasma were acquired by proton nuclear magnetic resonance (¹H-NMR) spectroscopy and analyzed by multivariate statistical data analysis. The relationship between demographic parameters and the potential metabolite biomarkers was further explored. Group antepartum or postpartum showed similar metabolic trends. Compare with those of the NGT group, the levels of 2-hydroxybutyrate, lysine, acetate, glutamine, succinate, tyrosine, formate, and all three BCAAs (leucine, valine, isoleucine) in the A2GDM group were increased dramatically, and the levels of lysine, acetate, and formate in the A1GDM group were elevated significantly. The dramatically decreased levels of 3-methyl-2-oxovalerate and methanol were observed both in the A1GDM group and A2GDM group. Compare to the A1GDM group, the branched-chain amino acids (BCAAs) of leucine, valine, and isoleucine were increased dramatically in the A2GDM group. The levels of aromatic amino acids (AAAs), tyrosine and phenylalanine, were significantly increased in GDM women, consistent with the severity of GDM. Interference of amino acid metabolism and disturbance in energy metabolism occurred in women with different grades of GDM. Metabolic profiles could reflect the severity of GDM. Plasma BCAA concentrations showing strong positive correlations with weight and pre-delivery BMI. This study provides a new perspective to understand the pathogenesis and etiology of GDM, which may help the clinical management and treatment of GDM.

Identification of key lipid metabolites during metabolic dysregulation in the diabetic retinopathy disease mouse model and efficacy of Keluoxin capsule using an UHPLC-MS-based non-targeted lipidomics approach

Diabetic retinopathy (DR) is an important complication of diabetes, and is currently the main cause of blindness among young adults in the world. Previous studies have shown that Keluoxin (KLX) capsules have a significant effect on DR in C57BL/KsJ/db-/- mice (db/db mice), however the unclear mechanism limits its further clinical application and actual value. Further research is urgently needed for the treatment of DR disease. Discovery of key lipid biomarkers and metabolic pathways can reveal and explore the molecular mechanisms related to DR development and discover the effect of Keluoxin (KLX) capsule against DR in db/db mice. Lipidomics has been used for characterizing the pathological conditions via identification of key lipid metabolites and the metabolic pathway. In this study, the high-throughput lipidomics using UHPLC-Q-TOF/MS combined with multivariate statistical analysis, querying multiple network databases and employing ingenuity pathway analysis (IPA) method for molecular target prediction. A total of 30 lipid biomarkers were identified and 7 metabolic pathways including arachidonic acid metabolism and steroid hormone biosynthesis were found. The preventive effect of KLX intervention can regulate 22 biomarkers such as LysoPA(16:0/0:0), prostaglandin D2, cortisol and γ-linolenic acid, etc. IPA platform has predicted that PI3K/MAPK pathway are closely related to DR development. It also showed that high-throughput lipidomics combined with multivariate statistical analysis could deep excavate of the biological significance of the big data, and can provide molecular targets information about the disease treatment.

Serum Metabolites as an Indicator of Developing Gestational Diabetes Mellitus Later in the Pregnancy: A Prospective Cohort of a Chinese Population

OBJECTIVE

Gestational diabetes mellitus (GDM) is a common metabolic disorder with onset during pregnancy. However, the etiology and pathogenesis of GDM have not been fully elucidated. In this study, we used a metabolomics approach to investigate the relationship between maternal serum metabolites and GDM in early pregnancy.

METHODS

A nested case-control study was performed. To establish an early pregnancy cohort, pregnant women in early pregnancy (10-13⁺⁶ weeks) were recruited. In total, 51 patients with GDM and 51 healthy controls were included. Serum samples were analyzed using an untargeted high-performance liquid chromatography mass spectrometry metabolomics approach. The relationships between metabolites and GDM were analyzed by an orthogonal partial least-squares discriminant analysis. Differential metabolites were evaluated using a KEGG pathway analysis.

RESULTS

A total of 44 differential metabolites were identified between GDM cases and healthy controls during early pregnancy. Of these, 26 significant metabolites were obtained in early pregnancy after false discovery rate (FDR < 0.1) correction. In the GDM group, the levels of L-pyroglutamic acid, L-glutamic acid, phenylacetic acid, pantothenic acid, and xanthine were significantly higher and the levels of 1,5-anhydro-D-glucitol, calcitriol, and 4-oxoproline were significantly lower than those in the control group. These metabolites were involved in multiple metabolic pathways, including those for amino acid, carbohydrate, lipid, energy, nucleotide, cofactor, and vitamin metabolism.

CONCLUSIONS

We identified significant differentially expressed metabolites associated with the risk of GDM, providing insight into the mechanisms underlying GDM in early pregnancy and candidate predictive markers.

Regulation of maternal-fetal metabolic communication

Pregnancy may be the most nutritionally sensitive stage in the life cycle, and improved metabolic health during gestation and early postnatal life can reduce the risk of chronic disease in adulthood. Successful pregnancy requires coordinated metabolic, hormonal, and immunological communication. In this review, maternal-fetal metabolic communication is defined as the bidirectional communication of nutritional status and metabolic demand by various modes including circulating metabolites, endocrine molecules, and other secreted factors. Emphasis is placed on metabolites as a means of maternal-fetal communication by synthesizing findings from studies in humans, non-human primates, domestic animals, rabbits, and rodents. In this review, fetal, placental, and maternal metabolic adaptations are discussed in turn. (1) Fetal macronutrient needs are summarized in terms of the physiological adaptations in place to ensure their proper allocation. (2) Placental metabolite transport and maternal physiological adaptations during gestation, including changes in energy budget, are also discussed. (3) Maternal nutrient limitation and metabolic disorders of pregnancy serve as case studies of the dynamic nature of maternal-fetal metabolic communication. The review concludes with a summary of recent research efforts to identify metabolites, endocrine molecules, and other secreted factors that mediate this communication, with particular emphasis on serum/plasma metabolomics in humans, non-human primates, and rodents. A better understanding of maternal-fetal metabolic communication in health and disease may reveal novel biomarkers and therapeutic targets for metabolic disorders of pregnancy.

Early-life exposure to widespread environmental toxicants and maternal-fetal health risk: A focus on metabolomic biomarkers

Prenatal exposure to widespread environmental toxicants is detrimental to maternal health and fetal development. The effects of environmental toxicants on maternal and fetal metabolic profile changes have not yet been summarized. This systematic review aims to summarize the current studies exploring the association between prenatal exposure to environmental toxicants and metabolic profile alterations in mother and fetus. We searched the MEDLINE (PubMed) electronic database for relevant literature conducted up to September 18, 2019 with some key terms. From the initial 155 articles, 15 articles met the inclusion and exclusion criteria, and consist of highly heterogeneous research methods. Seven studies assessed the effects of multiple environmental pollutants (metals, organic pollutants, nicotine, air pollutants) on the maternal urine and blood metabolomic profile; five studies evaluated the effects of arsenic, polychlorinated biphenyls (PCBs), nicotine, and ambient fine particulate matter (PM_2.5) on the cord blood metabolomic profile; and one study assessed the effects of smoking exposure on the amniotic fluid metabolomic profile. The alteration of metabolic pathways in these studies mainly involve energy metabolism, hormone metabolism, oxidative stress and inflammation. No population study investigated the association between environmental toxicants and placental metabolomics. This systematic review provides evidence that prenatal exposure to a variety of environmental pollutants can affect maternal and fetal metabolomic characteristics. Integration of environmental toxicant exposure and metabolomics data in maternal-fetal samples is helpful to understand the interaction between toxicants and metabolites, so as to reveal the pathogenesis of fetal disease or diseases of fetal origin.

Maternal Diet and the Serum Metabolome in Pregnancy: Robust Dietary Biomarkers Generalizable to a Multiethnic Birth Cohort

BACKGROUND

Advances in metabolomics are anticipated to decipher associations between dietary exposures and health. Replication biomarker studies in different populations are critical to demonstrate generalizability.

OBJECTIVES

To identify and validate robust serum metabolites associated with diet quality and specific foods in a multiethnic cohort of pregnant women.

DESIGN

In this cross-sectional analysis of 3 multiethnic Canadian birth cohorts, we collected semiquantitative FFQ and serum data from 900 women at the second trimester of pregnancy. We calculated a diet quality score (DQS), defined as daily servings of "healthy" minus "unhealthy" foods. Serum metabolomics was performed by multisegment injection-capillary electrophoresis-mass spectrometry, and specific serum metabolites associated with maternal DQSs were identified. We combined the results across all 3 cohorts using meta-analysis to classify robust dietary biomarkers (r > ± 0.1; P < 0.05).

RESULTS

Diet quality was higher in the South Asian birth cohort (mean DQS = 7.1) than the 2 white Caucasian birth cohorts (mean DQS <3.2). Sixty-six metabolites were detected with high frequency (>75%) and adequate precision (CV <30%), and 47 were common to all cohorts. Hippuric acid was positively associated with healthy diet score in all cohorts, and with the overall DQS only in the primarily white Caucasian cohorts. We observed robust correlations between: 1) proline betaine-citrus foods; 2) 3-methylhistidine-red meat, chicken, and eggs; 3) hippuric acid-fruits and vegetables; 4) trimethylamine N-oxide (TMAO)-seafood, meat, and eggs; and 5) tryptophan betaine-nuts/legumes.

CONCLUSIONS

Specific serum metabolites reflect intake of citrus fruit/juice, vegetables, animal foods, and nuts/legumes in pregnant women independent of ethnicity, fasting status, and delays to storage across multiple collection centers. Robust biomarkers of overall diet quality varied by cohort. Proline betaine, 3-methylhistidine, hippuric acid, TMAO, and tryptophan betaine were robust dietary biomarkers for investigations of maternal nutrition in diverse populations.

Differential mRNA and Long Noncoding RNA Expression Profiles in Umbilical Cord Blood Exosomes from Gestational Diabetes Mellitus Patients

Background and Aims: Exosomes contain numerous RNAs and transfer them between cells or organs, thereby establishing intercellular or interorgan communication. The roles of mRNAs and long noncoding RNAs (lncRNAs) from umbilical cord blood exosomes in gestational diabetes mellitus (GDM) occurrence and fetus growth remain poorly understood. We aimed to establish the differential mRNA and lncRNA expression profiles in umbilical cord blood exosomes from GDM patients compared with normal controls. Results: Using microarray technology, we identified 84 mRNAs and 256 lncRNAs as differentially expressed in umbilical cord blood exosomes of GDM patients compared with controls. The protein-protein interaction network revealed that the differentially expressed mRNAs were associated with glucagon signaling pathway, an important GDM-related pathway. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathway analyses were performed for mRNAs associated with differentially expressed lncRNAs. The results indicated that metabolic process, growth, and development were significantly enriched, which are important in GDM development and fetus growth. Moreover, pathway network was constructed to reveal the key pathways in GDM, such as metabolic pathways and insulin signaling pathway. Further lncRNA/miRNA interaction analysis showed that most of the exosomal lncRNAs harbored miRNA binding sites, and some were associated with GDM. Conclusion: These results showed that exosomal mRNAs and lncRNAs are aberrantly expressed in the umbilical cord blood of GDM patients and play potential roles in GDM development and fetus growth.

Underlying dyslipidemia postpartum in women with a recent GDM pregnancy who develop type 2 diabetes

Approximately, 35% of women with Gestational Diabetes (GDM) progress to Type 2 Diabetes (T2D) within 10 years. However, links between GDM and T2D are not well understood. We used a well-characterised GDM prospective cohort of 1035 women following up to 8 years postpartum. Lipidomics profiling covering >1000 lipids was performed on fasting plasma samples from participants 6–9 week postpartum (171 incident T2D vs. 179 controls). We discovered 311 lipids positively and 70 lipids negatively associated with T2D risk. The upregulation of glycerolipid metabolism involving triacylglycerol and diacylglycerol biosynthesis suggested activated lipid storage before diabetes onset. In contrast, decreased sphingomyelines, hexosylceramide and lactosylceramide indicated impaired sphingolipid metabolism. Additionally, a lipid signature was identified to effectively predict future diabetes risk. These findings demonstrate an underlying dyslipidemia during the early postpartum in those GDM women who progress to T2D and suggest endogenous lipogenesis may be a driving force for future diabetes onset.

The Human Breast Milk Metabolome in Overweight and Obese Mothers

Pre-pregnancy body mass index (BMI) is a major relevance factor, since maternal overweight and obesity can impair the pregnancy outcome and represent risk factors for several neonatal, childhood, and adult conditions, including excessive weight gain, cardiovascular disease, diabetes mellitus, and even behavioral disorders. Currently, breast milk (BM) composition in such category of mothers was not completely defined. In this field, metabolomics represents the ideal technology, able to detect the whole profile of low molecular weight molecules in BM. Limited information is available on human BM metabolites differences in overweight or obese compared to lean mothers. Analyzing all the metabolomics studies published on Medline in English language, this review evaluated the effects that 8 specific types of metabolites found altered by maternal overweight and obesity (nucleotide derivatives, 5-methylthioadenosine, sugar-alcohols, acylcarnitine and amino acids, polyamines, mono-and oligosaccharides, lipids) can exert on the risk of offspring obesity development and other potentially associated health outcomes and complications. However, metabolites variations in samples collected from overweight and obese mothers and the potentially correlated effects highlighted below still need further investigations and should be confirmed in future metabolomics studies on larger samples. Finally, the positive or negative influence of maternal overweight and obesity on the offspring, potentially exerted by breastfeeding, should be analyzed in close correlation with maternal age, genetic and environmental factors, including diet, and taking into account the interactions occurring between BM metabolites and lactobiome. The evaluation of all the factors affecting BM metabolites in overweight and obese mothers can lead to the comprehensive description of such biofluid and the related effects on breastfed subjects, potentially highlighting personalized needs of BM supplementation or short- and long-term prevention strategies to optimize offspring health.

Amino acids levels in early pregnancy predict subsequent gestational diabetes

BACKGROUND

We aimed to estimate the performance of amino acids levels in predicting the risk of subsequent gestational diabetes mellitus (GDM).

METHODS

A total of 431 women at 12 to 16 weeks of gestation in the Department of Obstetrics and Gynecology of the Second Affiliated Hospital of Soochow University were recruited. High-performance liquid chromatography electrospray tandem mass spectrometry was used to measure amino acids levels in maternal blood at 12 to 16 weeks of gestation. At 24 to 28 weeks of gestation, all participants were administered 75-g oral glucose tolerance tests for the diagnosis of GDM.

RESULTS

Alanine, isoleucine, and tyrosine levels in early pregnancy were significantly different between women who developed GDM and those who remained normal glucose tolerant. Logistic regressions showed that after adjustments for age, parity, body mass index, family history of diabetes, γ-glutamyltranspeptidase, triglycerides, fasting glucose and fasting insulin levels, alanine (odds ratio [OR], 1.46; 95% CI, 1.05-2.04; P = .027), isoleucine (OR, 1.48; 95% CI, 1.12-1.96; P = .0062), and tyrosine (OR, 1.46; 95% CI, 1.07-2.03; P = .020) levels in early pregnancy were independently associated with subsequent GDM. The addition of isoleucine and tyrosine into the conventional model improved the area under curve from 0.692 to 0.737 (P = .036) and significantly increased the net reclassification improvement (+13.7%, P = .0025).

CONCLUSIONS

The present study suggests that elevated isoleucine, tyrosine, and alanine levels are independently and significantly associated with subsequent incidence of GDM. New models including conventional risk factors, isoleucine and tyrosine levels in early pregnancy might help physicians identify high-risk population of GDM.

In vivo: maternal betaine supplementation normalized fetal growth in diabetic pregnancy

PURPOSE

Diabetes alters maternal metabolism and can lead to aberrant fetal growth. In addition to insulin treatment, nutritional diet interventions are recommended for promoting fetal health against diabetes-induced adverse effects. Therefore, we conducted an in vivo study to investigate betaine efficacy on fetal development against maternal diabetes.

METHODS

Thirty-two dams were divided into four equal groups: control (C), betaine supplementation (BS), diabetic pregnancy (DP) and diabetic pregnancy plus betaine supplementation (DP + BS). Fasting blood sugar (FBS) and body weight (BW) were monitored during pregnancy. After physiological delivery, dams glycated hemoglobin (HbA1c) concentrations were measured, followed by fetal development indices including litter size (LS), neonatal weight (NW) and crown-rump (CR). Also, maternal oxidative status was assessed by evaluating glutathione (GSH) content, glutathione peroxidase (GSH-Px) and catalase (CAT) activities, and malondialdehyde (MDA) concentration in the erythrocytes.

RESULTS

Betaine supplementation significantly alleviated FBS and tended to recover BW loss. It also significantly decreased HbA1c values in dams of DP + BS compared to DP group. Normalized fetal indices such as LS, NW and CR under betaine supplementation were associated with a significant increase in GSH content and GSH-Px activity, as well as decreased MDA concentrations in erythrocytes of dams in the DP + BS versus the DP group, indicating improved redox balance in the dams.

CONCLUSION

We indicated for the first time that betaine supplementation improved the maternal glucose metabolism and redox balance associated with normalized fetal growth. Nevertheless, further studies are required to investigate the mechanisms through which betaine protects fetal growth in diabetic pregnancy.