Diabetes Leads to Alterations in Normal Metabolic Transitions of Pregnancy as Revealed by Time-Course Metabolomics

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

BACKGROUND

Branched-chain amino acids (BCAAs), including leucine, isoleucine, and valine, are essential amino acids for mammals. Maternal BCAAs during pregnancy have been associated with newborn development. Meanwhile, BCAAs have been tightly linked with insulin resistance and diabetes in recent years. Diabetes in pregnancy is a common metabolic disorder. The current study aims to assess the circulating BCAA levels in pregnant women with diabetes and their relationship with neonatal development.

METHODS

The serum concentrations of BCAAs and their corresponding branched-chain α-keto acids (BCKAs) catabolites in 33 pregnant women with normal glucose tolerance, 16 pregnant women with type 2 diabetes before pregnancy (PDGM), and 15 pregnant women with gestational diabetes mellitus (GDM) were determined using a liquid chromatography system coupled to a mass spectrometer. The data were tested for normal distribution and homogeneity of variance before statistical analysis. Correlations were computed with the Pearson correlation coefficient.

RESULTS

The maternal serum BCAAs and BCKAs levels during late pregnancy were higher in women with PGDM than those in healthy women. Meanwhile, the circulating BCAAs and BCKAs showed no significant changes in women with GDM compared with those in healthy pregnant women. Furthermore, the circulating BCAA and BCKA levels in women with PGDM were positively correlated with the weight of the newborn. The circulating leucine level in women with GDM was positively correlated with the weight of the newborn. BCAA and BCKA levels in healthy pregnant women showed no correlation with newborn weight.

CONCLUSIONS

The serum BCAAs in pregnant women with diabetes, which was elevated in PGDM but not GDM, were positively correlated with newborn weight. These findings highlight potential approaches for early identification of high-risk individuals and interventions to reduce the risk of adverse pregnancy outcomes.

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.

Metabolic dynamics and prediction of sFGR and adverse fetal outcomes: a prospective longitudinal cohort study

BACKGROUND

Selective fetal growth restriction (sFGR) is an extreme complication that significantly increases the risk of perinatal mortality and long-term adverse neurological outcomes in offspring, affecting approximately 15% of monochorionic diamniotic (MCDA) twin pregnancies. The lack of longitudinal cohort studies hinders the early prediction and intervention of sFGR.

METHODS

We constructed a prospective longitudinal cohort study of sFGR, and quantified 25 key metabolites in 337 samples from maternal plasma in the first, second, and third trimester and from cord plasma. In particular, our study examined fetal growth and brain injury data from ultrasonography and used the Ages and Stages Questionnaire-third edition subscale (ASQ-3) to evaluate the long-term neurocognitive behavioral development of infants aged 2-3 years. Furthermore, we correlated metabolite levels with ultrasound data, including physical development and brain injury indicators, and ASQ-3 data using Spearman's-based correlation tests. In addition, special combinations of differential metabolites were used to construct predictive models for the occurrence of sFGR and fetal brain injury.

RESULTS

Our findings revealed various dynamic patterns for these metabolites during pregnancy and a maximum of differential metabolites between sFGR and MCDA in the second trimester (n = 8). The combination of L-phenylalanine, L-leucine, and L-isoleucine in the second trimester, which were closely related to fetal growth indicators, was highly predictive of sFGR occurrence (area under the curve [AUC]: 0.878). The combination of L-serine, L-histidine, and L-arginine in the first trimester and creatinine in the second trimester was correlated with long-term neurocognitive behavioral development and showed the capacity to identify fetal brain injury with high accuracy (AUC: 0.94).

CONCLUSIONS

The performance of maternal plasma metabolites from the first and second trimester is superior to those from the third trimester and cord plasma in discerning sFGR and fetal brain injury. These metabolites may serve as useful biomarkers for early prediction and promising targets for early intervention in clinical settings.

Salbutamol ameliorates skeletal muscle wasting and inflammatory markers in streptozotocin (STZ)-induced diabetic rats

Diabetes accelerates muscle atrophy, leading to the deterioration of skeletal muscles. This study aimed to assess the potential of the β2-adrenoceptor agonist, salbutamol (SLB), to alleviate muscle atrophy in streptozotocin (STZ)-induced diabetic rats. Male Sprague Dawley rats were randomized into four groups (n=6): control, SLB, STZ (55 mg/kg, single i.p.), and STZ + SLB (6 mg/kg, orally for 4 weeks). After the final SLB dose, animals underwent tests to evaluate muscle strength and coordination, including forelimb grip strength, wire-hanging, actophotometer, rotarod, and footprint assessments. Rats were then sacrificed, and serum and gastrocnemius (GN) muscles were collected for further analysis. Serum evaluations included proinflammatory markers (tumor necrosis factor α, interleukin-1β, interleukin-6), muscle markers (creatine kinase, myostatin), testosterone, and lipidemic markers. Muscle oxidative stress (malonaldehyde, protein carbonyl), antioxidants (glutathione, catalase, superoxide dismutase), and histology were also performed. Additionally, 1H nuclear magnetic resonance serum profiling was conducted. SLB notably enhanced muscle grip strength, coordination, and antioxidant levels, while reduced proinflammatory markers and oxidative stress in STZ-induced diabetic rats. Reduced serum muscle biomarkers, increased testosterone, restored lipidemic levels, and improved muscle cellular architecture indicated SLB's positive effect on muscle condition in diabetic rats. Metabolomics profiling revealed that the STZ group significantly increased the phenylalanine-to-tyrosine ratio (PTR), lactate-to-pyruvate ratio (LPR), acetate, succinate, isobutyrate, and histidine. SLB administration restored these perturbed serum metabolites in the STZ-induced diabetic group. In conclusion, salbutamol significantly protected against skeletal muscle wasting in STZ-induced diabetic rats.

DNA methylation analysis reveals the effect of arsenic on gestational diabetes mellitus

BACKGROUND

Arsenic (As) exposure is one of the risk factors for gestational diabetes mellitus (GDM). This study aimed to explore the effect of As-exposure on DNA methylation in GDM and to establish a risk assessment model of GDM in As exposed pregnant women.

METHOD

We collected elbow vein blood of pregnant women before delivery to measure As concentration and DNA methylation data. Then compared the DNA methylation data and established a nomogram.

RESULT

We identified a total of 10 key differentially methylated CpGs (DMCs) and found 6 corresponding genes. Functions were enriched in Hippo signaling pathway, cell tight junction, prophetic acid metabolism, ketone body metabolic process, and antigen processing and presentation. A nomogram was established that can predict GDM risks (c-index = 0.595, s:p = 0.973).

CONCLUSION

We found 6 genes associated with GDM with high As exposure. The prediction of the nomograms has been proven to be effective.

Metabolomics can provide new insights into perinatal nutrition

Perinatal nutrition is a key factor related to the Developmental Origin of Health and Disease hypothesis, which states that each and every event that happens during the periconceptional period and pregnancy can affect the health status of an individual. Metabolomics can be a very useful tool for gathering information about the effect of perinatal nutrition on both mothers and newborn infants. This non-systematic review focuses on the main metabolites detected by this technique, with regard to gestational diabetes, intrauterine growth restriction and breast milk. Conclusion. Nutrition, metabolome and microbiome interactions are gaining interest in the scientific community.

Metabolomics in gestational diabetes mellitus: A review

Gestational diabetes mellitus (GDM), a common complication of pregnancy, is a type of diabetes that is first detected and diagnosed during pregnancy. The incidence of GDM is increasing annually and is associated with many adverse pregnancy outcomes. Early prediction of the risk of GDM and intervention are thus important to reduce adverse pregnancy outcomes. Studies have revealed a correlation between the levels of amino acids, fatty acids, triglycerides, and other metabolites in early pregnancy and the occurrence of GDM. The development of high-throughput technologies used in metabolomics has enabled the detection of changes in the levels of small-molecule metabolites during early pregnancy, which can help reflect the overall physiological and pathological status of the body and explore the underlying mechanisms of the development of GDM. This review sought to summarize current research in this field and provide data for the development of strategies to manage GDM.

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.

Serum metabolomic signatures of gestational diabetes in South Asian and white European women

INTRODUCTION

This study aimed to identify serum metabolomic signatures associated with gestational diabetes mellitus (GDM), and to examine if ethnic-specific differences exist between South Asian and white European women.

RESEARCH DESIGN AND METHODS

Prospective cohort study with a nested case-control analysis of 600 pregnant women from two Canadian birth cohorts; using an untargeted approach, 63 fasting serum metabolites were measured and analyzed using multisegment injection-capillary electrophoresis-mass spectrometry. Multivariate logistic regression modeling was conducted overall and by cohort.

RESULTS

The proportion of women with GDM was higher in South Asians (27.1%) compared with white Europeans (17.9%). Several amino acid, carbohydrate, and lipid pathways related to GDM were common to South Asian and white European women. Elevated circulating concentrations of glutamic acid, propionylcarnitine, tryptophan, arginine, 2-hydroxybutyric acid, 3-hydroxybutyric acid, and 3-methyl-2-oxovaleric acid were associated with higher odds of GDM, while higher glutamine, ornithine, oxoproline, cystine, glycine with lower odds of GDM. Per SD increase in glucose concentration, the odds of GDM increased (OR=2.07, 95% CI 1.58 to 2.71), similarly for metabolite ratios: glucose to glutamine (OR=2.15, 95% CI 1.65 to 2.80), glucose to creatinine (OR=1.79, 95% CI 1.39 to 2.32), and glutamic acid to glutamine (OR=1.46, 95% CI 1.16 to 1.83). South Asians had higher circulating ratios of glucose to glutamine, glucose to creatinine, arginine to ornithine, and citrulline to ornithine, compared with white Europeans.

CONCLUSIONS

We identified a panel of serum metabolites implicated in GDM pathophysiology, consistent in South Asian and white European women. The metabolic alterations leading to larger ratios of glucose to glutamine, glucose to creatinine, arginine to ornithine, and citrulline to ornithine in South Asians likely reflect the greater burden of GDM among South Asians compared with white Europeans.

Effects of Tang Luo Ning on diabetic peripheral neuropathy in rats revealed by LC-MS metabolomics approach

Diabetic peripheral neuropathy (DPN) is one of the most common complications of diabetes with limited therapies. Tang Luo Ning (TLN), a traditional Chinese medicine compound, has been proved to be effective in the treatment of DPN in clinical and experimental studies. However, the potential metabolic mechanism of TLN for the treatment of DPN is still unclear. Here the therapeutic effect of TLN on DPN was studied, and HPLC-IT-TOF/MS was used to explore the metabolic changes related to DPN and to explore the mechanism of TLN on DPN induced by high glucose. Furthermore, metabolic pathway analysis was used to explore the metabolic changes induced by DPN and TLN. As a result, TLN could improve the peripheral nerve function of DPN rats, and TLN could reduce the demyelination of the sciatic nerve in DPN rats. Metabolomics analysis showed that 14 potential biomarkers (citrate, creatine, fumarate, glyceric acid, glycine, succinate, etc.) of both DPN and TLN treatment were identified. Pathway analysis showed that the changes in these metabolites were mainly related to the citrate cycle (TCA cycle), glycine, serine and threonine metabolism, and glyoxylate and dicarboxylate metabolism.

Pregnancy Complications, Correlation With Placental Pathology and Neonatal Outcomes

Purpose

We aimed to clarify and contribute to a better comprehension of associations and correlations between placental histological findings, pregnancy evolution, and neonatal outcomes.

Study Design

This is a longitudinal and prospective observational study, performed between May 2015 and May 2019, on 506 pregnant women. Clinical data related to pregnancy outcome, neonatal health status, and placental histology were primarily collected. Twin pregnancies or malformed newborns were excluded and therefore the study was conducted on 439 cases. These cases have been then subdivided into the following study groups: (a) 282 placentas from pathological pregnancies; and, (b) a control group of 157 pregnancies over 33 weeks of gestational age, defined as physiological or normal pregnancies due to the absence of maternal, fetal, and early neonatal pathologies, most of which had undergone elective cesarean section for maternal or fetal indication.

Results

A normal placenta was present in 57.5% of normal pregnancies and in 42.5% of pathological pregnancies. In contrast, placental pathology was present in 26.2% of normal pregnancies and 73.8% of pathological pregnancies. Comparison of the neonatal health status with the pregnancy outcome showed that, among the 191 newborns classified as normal, 98 (51.3%) were born from a normal pregnancy, while 93 (48.7%) were born from mothers with a pathological pregnancy. Among the 248 pathological infants, 59 (23.8%) were born from a mother with a normal pregnancy, while 189 (76.2%) were born from pregnancies defined as pathological.

Conclusion

Placental histology must be better understood in the context of natural history of disease. Retrospective awareness of placental damage is useful in prevention in successive pregnancy, but their early identification in the evolving pregnancy could help in association with biological markers or more sophisticated instruments for early diagnosis.

Dynamic changes and early predictive value of branched-chain amino acids in gestational diabetes mellitus during pregnancy

OBJECTIVE

Branched-chain amino acids (BCAAs) are closely associated with type 2 diabetes mellitus, but their roles in gestational diabetes mellitus (GDM) are still controversial. This study aims to explore the dynamic changes of BCAAs during pregnancy and identify potential early biomarkers for GDM.

METHODS

This study is a nested case-control study involved 49 women with GDM and 50 age- and body mass index (BMI)-matched healthy pregnant women. The dynamic changes of valine (Val), isoleucine (Ile), and leucine (Leu) were detected in the first (8-12 weeks) and second trimesters (24-28 weeks) by liquid chromatography-mass spectrometry.

RESULTS

Serum Val, Ile, and Leu were higher in GDM patients than in controls in the first trimester. Compared with the first trimester, the serum Val, Ile, and Leu in GDM patients were decreased in the second trimester. In addition, Val, Ile, and Leu in the first trimester were the risk factors for GDM, and Ile presented a high predictive value for GDM. Ile + age (≥ 35) + BMI (≥ 24) exhibited the highest predictive value for GDM (AUC = 0.902, sensitivity = 93.9%, specificity = 80%).

CONCLUSION

Maternal serum Ile in the first trimester was a valuable biomarker for GDM. Ile combined with advanced maternal age and overweight may be used for the early prediction of GDM.

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.

Urinary Metabolomic Profile of Neonates Born to Women with Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications with potential adverse outcomes for mothers and newborns. Its effects on the newborn appear during the neonatal period or early childhood. Therefore, an early diagnosis is crucial to prevent the development of chronic diseases later in adult life. In this study, the urinary metabolome of babies born to GDM mothers was characterized. In total, 144 neonatal and maternal (second and third trimesters of pregnancy) urinary samples were analyzed using targeted metabolomics, combining liquid chromatographic mass spectrometry (LC-MS/MS) and flow injection analysis mass spectrometry (FIA-MS/MS) techniques. We provide here the neonatal urinary concentration values of 101 metabolites for 26 newborns born to GDM mothers and 22 newborns born to healthy mothers. The univariate analysis of these metabolites revealed statistical differences in 11 metabolites. Multivariate analyses revealed a differential metabolic profile in newborns of GDM mothers characterized by dysregulation of acylcarnitines, amino acids, and polyamine metabolism. Levels of hexadecenoylcarnitine (C16:1) and spermine were also higher in newborns of GDM mothers. The maternal urinary metabolome revealed significant differences in butyric, isobutyric, and uric acid in the second and third trimesters of pregnancy. These metabolic alterations point to the impact of GDM in the neonatal period.

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.