UPLC-MS metabolic profiling of second trimester amniotic fluid and maternal urine and comparison with NMR spectral profiling for the identification of pregnancy disorder biomarkers

Metabolomic analysis of the human placenta reveals perturbations in amino acids, purine metabolites, and small organic acids in spontaneous preterm birth

Spontaneous preterm delivery presents one of the most complex challenges in obstetrics and is a leading cause of perinatal morbidity and mortality. Although it is a common endpoint for multiple pathological processes, the mechanisms governing the etiological complexity of spontaneous preterm birth and the placental responses are poorly understood. This study examined placental tissues collected between May 2019 and May 2022 from a well-defined cohort of women who experienced spontaneous preterm birth (n = 72) and healthy full-term deliveries (n = 30). Placental metabolomic profiling of polar metabolites was performed using Ultra-High Performance Liquid Chromatography/Mass Spectrometry (UHPLC/MS) analysis. The resulting data were analyzed using multi- and univariate statistical methods followed by unsupervised clustering. A comprehensive metabolomic evaluation of the placenta revealed that spontaneous preterm birth was associated with significant changes in the levels of 34 polar metabolites involved in intracellular energy metabolism and biochemical activity, including amino acids, purine metabolites, and small organic acids. We found that neither the preterm delivery phenotype nor the inflammatory response explain the reported differential placental metabolome. However, unsupervised clustering revealed two molecular subtypes of placentas from spontaneous preterm pregnancies exhibiting differential enrichment of clinical parameters. We also identified differences between early and late preterm samples, suggesting distinct placental functions in early spontaneous preterm delivery. Altogether, we present evidence that spontaneous preterm birth is associated with significant changes in the level of placental polar metabolites. Dysregulation of the placental metabolome may underpin important (patho)physiological mechanisms involved in preterm birth etiology and long-term neonatal outcomes.

Metabolomic Profiling of Second-Trimester Amniotic Fluid for Predicting Preterm Delivery: Insights from NMR Analysis

Preterm delivery (PTD) is a notable pregnancy complication, affecting one out of every ten births. This study set out to investigate whether analyzing the metabolic composition of amniotic fluid (AF) collected from pregnant women during the second trimester of pregnancy could offer valuable insights into prematurity. The research employed 1H-NMR metabolomics to examine AF samples obtained from 17 women who gave birth prematurely (between 29+0 and 36+5 weeks of gestation) and 43 women who delivered at full term. The application of multivariate analysis revealed metabolites (dimethylglycine, glucose, myo-inositol, and succinate) that can serve as possible biomarkers for the prognosis and early diagnosis of preterm delivery. Additionally, pathway analysis unveiled the most critical metabolic pathways relevant to our research hypothesis. In summary, these findings suggest that the metabolic composition of AF in the second trimester can be a potential indicator for identifying biomarkers associated with the risk of PTD.

A screening test proposal for congenital defects based on maternal serum metabolomics profile

BACKGROUND

Historically, noninvasive techniques are only able to identify chromosomal anomalies that accounted for <50% of all congenital defects; the other congenital defects are diagnosed via ultrasound evaluations in the later stages of pregnancy. Metabolomic analysis may provide an important improvement, potentially addressing the need for novel noninvasive and multicomprehensive early prenatal screening tools. A growing body of evidence outlines notable metabolic alterations in different biofluids derived from pregnant women carrying fetuses with malformations, suggesting that such an approach may allow the discovery of biomarkers common to most fetal malformations. In addition, metabolomic investigations are inexpensive, fast, and risk-free and often generate high performance screening tests that may allow early detection of a given pathology.

OBJECTIVE

This study aimed to evaluate the diagnostic accuracy of an ensemble machine learning model based on maternal serum metabolomic signatures for detecting fetal malformations, including both chromosomal anomalies and structural defects.

STUDY DESIGN

This was a multicenter observational retrospective study that included 2 different arms. In the first arm, a total of 654 Italian pregnant women (334 cases with fetuses with malformations and 320 controls with normal developing fetuses) were enrolled and used to train an ensemble machine learning classification model based on serum metabolomics profiles. In the second arm, serum samples obtained from 1935 participants of the New Zealand Screening for Pregnancy Endpoints study were blindly analyzed and used as a validation cohort. Untargeted metabolomics analysis was performed via gas chromatography-mass spectrometry. Of note, 9 individual machine learning classification models were built and optimized via cross-validation (partial least squares-discriminant analysis, linear discriminant analysis, naïve Bayes, decision tree, random forest, k-nearest neighbor, artificial neural network, support vector machine, and logistic regression). An ensemble of the models was developed according to a voting scheme statistically weighted by the cross-validation accuracy and classification confidence of the individual models. This ensemble machine learning system was used to screen the validation cohort.

RESULTS

Significant metabolic differences were detected in women carrying fetuses with malformations, who exhibited lower amounts of palmitic, myristic, and stearic acids; N-α-acetyllysine; glucose; L-acetylcarnitine; fructose; para-cresol; and xylose and higher levels of serine, alanine, urea, progesterone, and valine (P<.05), compared with controls. When applied to the validation cohort, the screening test showed a 99.4%±0.6% accuracy (specificity of 99.9%±0.1% [1892 of 1894 controls correctly identified] with a sensitivity of 78%±6% [32 of 41 fetal malformations correctly identified]).

CONCLUSION

This study provided clinical validation of a metabolomics-based prenatal screening test to detect the presence of congenital defects. Further investigations are needed to enable the identification of the type of malformation and to confirm these findings on even larger study populations.

Diagnostic Biomarkers for Gestational Diabetes Mellitus Using Spectroscopy Techniques: A Systematic Review

Gestational diabetes mellitus (GDM) is associated with adverse maternal and foetal consequences, along with the subsequent risk of type 2 diabetes mellitus (T2DM) and several other diseases. Due to early risk stratification in the prevention of progression of GDM, improvements in biomarker determination for GDM diagnosis will enhance the optimization of both maternal and foetal health. Spectroscopy techniques are being used in an increasing number of applications in medicine for investigating biochemical pathways and the identification of key biomarkers associated with the pathogenesis of GDM. The significance of spectroscopy promises the molecular information without the need for special stains and dyes; therefore, it speeds up and simplifies the necessary ex vivo and in vivo analysis for interventions in healthcare. All the selected studies showed that spectroscopy techniques were effective in the identification of biomarkers through specific biofluids. Existing GDM prediction and diagnosis through spectroscopy techniques presented invariable findings. Further studies are required in larger, ethnically diverse populations. This systematic review provides the up-to-date state of research on biomarkers in GDM, which were identified via various spectroscopy techniques, and a discussion of the clinical significance of these biomarkers in the prediction, diagnosis, and management of GDM.

Association between aberrant amino acid metabolism and nonchromosomal modifications fetal structural anomalies: A cohort study

BACKGROUND

More than half of the cases of fetal structural anomalies have no known cause with standard investigations like karyotype testing and chromosomal microarray. The differential metabolic profiles of amniotic fluid (AF) and maternal blood may reveal valuable information about the physiological processes of fetal development, which may provide valuable biomarkers for fetal health diagnostics.

METHODS

This cohort study of singleton-pregnant women had indications for amniocentesis, including structural anomalies and a positive result from maternal serum screening or non-invasive prenatal testing, but did not have any positive abnormal karyotype or chromosomal microarray analysis results. A total of 1580 participants were enrolled between June 2021 and March 2022. Of the 1580 pregnant women who underwent amniocentesis, 294 were included in the analysis. There were 137 pregnant women in the discovery cohort and 157 in the validation cohort.

RESULTS

High-coverage untargeted metabolomic analysis of AF revealed distinct metabolic signatures with 321 of the 602 metabolites measured (53%) (false discovery rate, q < 0.005), among which amino acids predominantly changed in structural anomalies. Targeted metabolomics identified glutamate and glutamine as novel predictive markers for structural anomalies, their vital role was also confirmed in the validation cohort with great predictive ability, and the area under the receiver operating characteristic curves (AUCs) were 0.862 and 0.894 respectively. And AUCs for glutamine/glutamate were 0.913 and 0.903 among the two cohorts.

CONCLUSIONS

Our results suggested that the aberrant glutamine/glutamate metabolism in AF is associated with nonchromosomal modificantions fetal structural anomalies. Based on our findings, a novel screening method could be established for the nonchromosomal modificantions fetal structural anomalies. And the results also indicate that monitoring fetal metabolic conditions (especially glutamine and glutamine metabolism) may be helpful for antenatal diagnosis and therapy.

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.

Systematic review of preterm birth multi-omic biomarker studies

Preterm birth (PTB) is one of the leading causes of deaths in infants under the age of five. Known risk factors of PTB include genetic factors, lifestyle choices or infection. Identification of omic biomarkers associated with PTB could aid clinical management of women at high risk of early labour and thereby reduce neonatal morbidity. This systematic literature review aimed to identify and summarise maternal omic and multi-omic (genomics, transcriptomics, proteomics and metabolites) biomarker studies of PTB. Original research articles were retrieved from three databases: PubMed, Web of Science and Science Direct, using specified search terms for each omic discipline. PTB studies investigating genomics, transcriptomics, proteomics or metabolomics biomarkers prior to onset of labour were included. Data were collected and reviewed independently. Pathway analyses were completed on the biomarkers from non-targeted omic studies using Reactome pathway analysis tool. A total of 149 omic studies were identified; most of the literature investigated proteomic biomarkers. Pathway analysis identified several cellular processes associated with the omic biomarkers reported in the literature. Study heterogeneity was observed across the research articles, including the use of different gestation cut-offs to define PTB. Infection/inflammatory biomarkers were identified across majority of papers using a range of targeted and non-targeted approaches.

Rapid and Label-Free Prenatal Detection of Down's Syndrome Using Body Fluid Surface Enhanced Raman Spectroscopy

Down's syndrome (DS) is the leading genetic cause of intellectual disability. In this work, the surface enhanced Raman spectroscopy (SERS) was used for the detection of amniotic fluid and plasma from pregnant women with DS fetus for the first time. High-quality and characteristic spectral features of amniotic fluid and plasma samples from DS groups can be obtained in comparison to normal group. Moreover, principal component analysis with linear discriminant analysis was applied to generate the efficient diagnostic model, achieving accuracies of 94.3% and 88.5% for the DS detection with amniotic fluid and plasma samples, respectively. This preliminary study would provide a novel, convenient and accurate prenatal test based on blood SERS technology for clinical DS screening.

Adaptive Changes in Glucose Homeostasis and Islet Function During Pregnancy: A Targeted Metabolomics Study in Mice

OBJECTIVE

Pregnancy is a dynamic state involving multiple metabolic adaptions in various tissues including the endocrine pancreas. However, a detailed characterization of the maternal islet metabolome in relation to islet function and the ambient circulating metabolome during pregnancy has not been established.

METHODS

A timed-pregnancy mouse model was studied, and age-matched non-pregnant mice were used as controls. Targeted metabolomics was applied to fasting plasma and purified islets during each trimester of pregnancy. Glucose homeostasis and islet function was assessed. Bioinformatic analyses were performed to reveal the metabolic adaptive changes in plasma and islets, and to identify key metabolic pathways associated with pregnancy.

RESULTS

Fasting glucose and insulin were found to be significantly lower in pregnant mice compared to non-pregnant controls, throughout the gestational period. Additionally, pregnant mice had superior glucose excursions and greater insulin response to an oral glucose tolerance test. Interestingly, both alpha and beta cell proliferation were significantly enhanced in early to mid-pregnancy, leading to significantly increased islet size seen in mid to late gestation. When comparing the plasma metabolome of pregnant and non-pregnant mice, phospholipid and fatty acid metabolism pathways were found to be upregulated throughout pregnancy, whereas amino acid metabolism initially decreased in early through mid pregnancy, but then increased in late pregnancy. Conversely, in islets, amino acid metabolism was consistently enriched throughout pregnancy, with glycerophospholid and fatty acid metabolism was only upregulated in late pregnancy. Specific amino acids (glutamate, valine) and lipids (acyl-alkyl-PC, diacyl-PC, and sphingomyelin) were found to be significantly differentially expressed in islets of the pregnant mice compared to controls, which was possibly linked to enhanced insulin secretion and islet proliferation.

CONCLUSION

Beta cell proliferation and function are elevated during pregnancy, and this is coupled to the enrichment of islet metabolites and metabolic pathways primarily associated with amino acid and glycerophospholipid metabolism. This study provides insight into metabolic adaptive changes in glucose homeostasis and islet function seen during pregnancy, which will provide a molecular rationale to further explore the regulation of maternal metabolism to avoid the onset of pregnancy disorders, including gestational diabetes.

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.

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

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

Metabolic profiling of maternal serum of women at high-risk of spontaneous preterm birth using NMR and MGWAS approach

Preterm birth (PTB) is a leading global cause of infant mortality. Risk factors include genetics, lifestyle choices and infection. Understanding the mechanism of PTB could aid the development of novel approaches to prevent PTB. This study aimed to investigate the metabolic biomarkers of PTB in early pregnancy and the association of significant metabolites with participant genotypes. Maternal sera collected at 16 and 20 weeks of gestation, from women who previously experienced PTB (high-risk) and women who did not (low-risk controls), were analysed using 1H nuclear magnetic resonance (NMR) metabolomics and genome-wide screening microarray. ANOVA and probabilistic neural network (PNN) modelling were performed on the spectral bins. Metabolomics genome-wide association (MGWAS) of the spectral bins and genotype data from the same participants was applied to determine potential metabolite-gene pathways. Phenylalanine, acetate and lactate metabolite differences between PTB cases and controls were obtained by ANOVA and PNN showed strong prediction at week 20 (AUC = 0.89). MGWAS identified several metabolite bins with strong genetic associations. Cis-eQTL analysis highlighted TRAF1 (involved in the inflammatory pathway) local to a non-coding SNP associated with lactate at week 20 of gestation. MGWAS of a well-defined cohort of participants highlighted a lactate-TRAF1 relationship that could potentially contribute to PTB.

Metabolomics Analysis of Amniotic Fluid in Euploid Foetuses with Thickened Nuchal Translucency by Gas Chromatography-Mass Spectrometry

Persistence of a fetal thickened nuchal translucency (NT), one of the most sensitive and specific individual markers of fetal disorders, is strongly correlated with the possibility of a genetic syndrome, congenital infections, or other malformations. Thickened NT can also be found in normal pregnancies. Several of its pathophysiological aspects still remain unexplained. Metabolomics could offer a fresh opportunity to explore maternal-foetal metabolism in an effort to explain its physiological and pathological mechanisms. For this prospective case-control pilot study, thirty-nine samples of amniotic fluids were collected, divisible into 12 euploid foetuses with an enlarged nuchal translucency (>NT) and 27 controls (C). Samples were analyzed using gas chromatography mass spectrometry. Multivariate and univariate statistical analyses were performed to find a specific metabolic pattern of >NT class. The correlation between the metabolic profile and clinical parameters was evaluated (NT showed an R2 = 0.75, foetal crown-rump length showed R2 = 0.65, pregnancy associated plasma protein-A showed R2 = 0.60). Nine metabolites significantly differing between >NT foetuses and C were detected: 2-hydroxybutyric acid, 3-hydroxybutyric, 1,5 Anydro-Sorbitol, cholesterol, erythronic acid, fructose, malic acid, threitol, and threonine, which were linked to altered pathways involved in altered energetic pathways. Through the metabolomics approach, it was possible to identify a specific metabolic fingerprint of the fetuses with >NT.

Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling

BACKGROUND

Gestational diabetes mellitus (GDM) is a type of glucose intolerance disorder that first occurs during women's pregnancy. The main diagnostic method for GDM is based on the midpregnancy oral glucose tolerance test. The rise of metabolomics has expanded the opportunity to better identify early diagnostic biomarkers and explore possible pathogenesis.

METHODS

We collected blood serum from 34 GDM patients and 34 normal controls for a LC-MS-based metabolomics study.

RESULTS

184 metabolites were increased and 86 metabolites were decreased in the positive ion mode, and 65 metabolites were increased and 71 were decreased in the negative ion mode. Also, it was found that the unsaturated fatty acid metabolism was disordered in GDM. Ten metabolites with the most significant differences were selected for follow-up studies. Since the diagnostic specificity and sensitivity of a single differential metabolite are not definitive, we combined these metabolites to prepare a ROC curve. We found a set of metabolite combination with the highest sensitivity and specificity, which included eicosapentaenoic acid, docosahexaenoic acid, docosapentaenoic acid, arachidonic acid, citric acid, α-ketoglutaric acid, and genistein. The area under the curves (AUC) value of those metabolites was 0.984 between the GDM and control group.

CONCLUSIONS

Our results provide a direction for the mechanism of GDM research and demonstrate the feasibility of developing a diagnostic test that can distinguish between GDM and normal controls clearly. Our findings were helpful to develop novel biomarkers for precision or personalized diagnosis for GDM. In addition, we provide a critical insight into the pathological and biological mechanisms for GDM.

Applying metabolomics to veterinary pharmacology and therapeutics

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

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.

Metabolomic profiles of mid-trimester amniotic fluid are not associated with subsequent spontaneous preterm delivery or gestational duration at delivery

INTRODUCTION

Spontaneous preterm delivery (<37 gestational weeks) has a multifactorial etiology with still incompletely identified pathways. Amniotic fluid is a biofluid with great potential for insights into the feto-maternal milieu. It is rich in metabolites, and metabolic consequences of inflammation is yet researched only to a limited extent. Metabolomic profiling provides opportunities to identify potential biomarkers of inflammatory conditioned pregnancy complications such as spontaneous preterm delivery.

OBJECTIVE

The aim of this study was to perform metabolomic profiling of amniotic fluid from uncomplicated singleton pregnancies in the mid-trimester to identify potential biomarkers associated with spontaneous preterm delivery and gestational duration at delivery. A secondary aim was to replicate previously reported mid-trimester amniotic fluid metabolic biomarkers of spontaneous preterm delivery in asymptomatic women.

METHOD

A nested case-control study was performed within a larger cohort study of asymptomatic pregnant women undergoing mid-trimester genetic amniocentesis at 14-19 gestational weeks in Gothenburg, Sweden. Medical records were used to obtain clinical data and delivery outcome variables. Amniotic fluid samples from women with a subsequent spontaneous preterm delivery (n = 37) were matched with amniotic fluid samples from women with a subsequent spontaneous delivery at term (n = 37). Amniotic fluid samples underwent untargeted metabolomic analyses using liquid chromatography-mass spectrometry. Multivariate random forest analyses were used for data processing. A secondary targeted analysis was performed, aiming to replicate previously reported mid-trimester amniotic fluid metabolic biomarkers in women with a subsequent spontaneous preterm delivery.

RESULTS

Multivariate analysis did not distinguish the samples from women with a subsequent spontaneous preterm delivery from those with a subsequent term delivery. Neither was the metabolic profile associated with gestational duration at delivery. Potential metabolic biomarker candidates were identified from four publications by two different research groups relating mid-trimester amniotic fluid metabolomes to spontaneous PTD, of which fifteen markers were included in the secondary analysis. None of these were replicated.

CONCLUSIONS

Metabolomic profiles of early mid-trimester amniotic fluid were not associated with spontaneous preterm delivery or gestational duration at delivery in this cohort.

Maternal plasma metabolic markers of neonatal adiposity and associated maternal characteristics: The GUSTO study

Infant adiposity may be related to later metabolic health. Maternal metabolite profiling reflects both genetic and environmental influences and allows elucidation of metabolic pathways associated with infant adiposity. In this multi-ethnic Asian cohort, we aimed to (i) identify maternal plasma metabolites associated with infant adiposity and other birth outcomes and (ii) investigate the maternal characteristics associated with those metabolites. In 940 mother-offspring pairs, we performed gas chromatography-mass spectrometry and identified 134 metabolites in maternal fasting plasma at 26–28 weeks of gestation. At birth, neonatal triceps and subscapular skinfold thicknesses were measured by trained research personnel, while weight and length measures were abstracted from delivery records. Gestational age was estimated from first-trimester dating ultrasound. Associations were assessed by multivariable linear regression, with p-values corrected using the Benjamini-Hochberg approach. At a false discovery rate of 5%, we observed associations between 28 metabolites and neonatal sum of skinfold thicknesses (13 amino acid-related, 4 non-esterified fatty acids, 6 xenobiotics, and 5 unknown compounds). Few associations were observed with gestational duration, birth weight, or birth length. Maternal ethnicity, pre-pregnancy BMI, and diet quality during pregnancy had the strongest associations with the specific metabolome related to infant adiposity. Further studies are warranted to replicate our findings and to understand the underlying mechanisms.

Study on amniotic fluid metabolism in the second trimester of Trisomy 21

BACKGROUND

Trisomy 21 is a common aneuploid condition in humans and accounts for approximately one quarter of all aneuploid live births. To date, early diagnosis of Trisomy 21 remains a challenging task. Metabolomics may prove an innovative tool to study the early pathophysiology of Trisomy 21 at a functional level.

METHODS

Ultra-performance liquid chromatography coupled with mass spectrometer (UPLC-MS) was used for untargeted metabolomic analysis of amniotic fluid samples from women having normal and trisomy 21 fetuses.

RESULTS

Many significantly changed metabolites were identified between amniotic fluid samples from Trisomy 21 pregnancies and normal euploid pregnancies, such as generally lower levels of several steroid hormones and their derivatives, higher levels of glutathione catabolites coupled with lower levels of gamma-glutamyl amino acids, and increased levels of phospholipid catabolites, sugars, and dicarboxylic acids. The identification of a human milk oligosaccharide in amniotic fluid may worth further investigation, since confirmation of this observation may have significant implications for regulation of fetal development.

CONCLUSIONS

The metabolisms in amniotic fluid from Trisomy 21 and normal pregnancies are quite different, and some of the significantly changed metabolites may be considered as candidates of early diagnostic biomarkers for Trisomy 21.

Assessing Urinary Metabolomics in Giant Pandas Using Chromatography/Mass Spectrometry: Pregnancy-Related Changes in the Metabolome

Giant pandas represent one of the most endangered species worldwide, and their reproductive capacity is extremely low. They have a relatively long gestational period, mainly because embryo implantation is delayed. Giant panda cubs comprise only a small proportion of the mother's body weight, making it difficult to determine whether a giant panda is pregnant. Timely determination of pregnancy contributes to the efficient breeding and management of giant pandas. Meanwhile, metabolomics studies the metabolic composition of biological samples, which can reflect metabolic functions in cells, tissues, and organisms. This work explored the urinary metabolites of giant pandas during pregnancy. A sample of 8 female pandas was selected. Differences in metabolite levels in giant panda urine samples were analyzed via ultra-high-performance liquid chromatography/mass spectrometry comparing pregnancy to anoestrus. Pattern recognition techniques, including partial least squares-discriminant analysis and orthogonal partial least squares-discriminant analysis, were used to analyze multiple parameters of the data. Compared with the results during anoestrus, multivariate statistical analysis of results obtained from the same pandas being pregnant identified 16 differential metabolites in the positive-ion mode and 43 differential metabolites in the negative-ion mode. The levels of tryptophan, choline, kynurenic acid, uric acid, indole-3-acetaldehyde, taurine, and betaine were higher in samples during pregnancy, whereas those of xanthurenic acid and S-adenosylhomocysteine were lower. Amino acid metabolism, lipid metabolism, and organic acid production differed significantly between anoestrus and pregnancy. Our results provide new insights into metabolic changes in the urine of giant pandas during pregnancy, and the differential levels of metabolites in urine provide a basis for determining pregnancy in giant pandas. Understanding these metabolic changes could be helpful for managing pregnant pandas to provide proper nutrients to their fetuses.

Metabolomics to reveal biomarkers and pathways of preterm birth: a systematic review and epidemiologic perspective

INTRODUCTION

Most known risk factors for preterm birth, a leading cause of infant morbidity and mortality, are not modifiable. Advanced molecular techniques are increasingly being applied to identify biomarkers and pathways important in disease development and progression.

OBJECTIVES

We review the state of the literature and assess it from an epidemiologic perspective.

METHODS

PubMed, Embase, CINAHL, and Cochrane Central were searched on January 31, 2019 for original articles published after 1998 that utilized an untargeted metabolomic approach to identify markers of preterm birth. Eligible manuscripts were peer-reviewed and included original data from untargeted metabolomics analyses of maternal tissue derived from human studies designed to determine mechanisms and predictors of preterm birth.

RESULTS

Of 2823 results, 14 articles met the inclusion requirements. There was little consistency in study design, outcome definition, type of biospecimen, or the inclusion of covariates and confounding factors, and few consistent associations with metabolites were identified in this review.

CONCLUSION

Studies to date on metabolomic predictors of preterm birth are highly heterogeneous in both methodology and resulting metabolite identification. There is an urgent need for larger studies in well-defined populations, to determine biomarkers predictive of preterm birth, and to reveal mechanisms and targets for development of intervention strategies.

Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and "omics" sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent "boom" in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.

Urinary Metabolites Altered during the Third Trimester in Pregnancies Complicated by Gestational Diabetes Mellitus: Relationship with Potential Upcoming Metabolic Disorders

Gestational diabetes mellitus (GDM) is a disorder in pregnancy with highest impact in the future life of both mother and newborn. Increasing incidence, economic impact, and potential for severe GDM-related pregnancy complications are some factors that have motivated the deep study of physiopathology, risk factors for developing GDM, and potential biomarkers for its diagnosis. In the present pilot study, we analyzed the urinary metabolome profile of GDM patients in the 3rd trimester of pregnancy, when GDM is already established and the patients are under dietary and pharmacological control. An untargeted metabolomics method based on liquid chromatography–mass spectrometry analysis was developed to identify differentially expressed metabolites in the GDM group. We identified 14 metabolites that are significantly upregulated in the urine of GDM patients, and, more importantly, we identified those related with the steroid hormone biosynthesis and tryptophan (TRP) metabolism pathways, which are associated with GDM pathophysiology. Thus, these metabolites could be screened as potential prognostic biomarkers of type two diabetes mellitus, coronary artery disease and chronic renal failure in future follow-up studies with GDM patients.

Exploration of predictive metabolic factors for gestational diabetes mellitus in Japanese women using metabolomic analysis

AIMS/INTRODUCTION

We aimed to explore novel predictive markers for gestational diabetes mellitus using metabolomic analysis in pregnant Japanese women.

MATERIALS AND METHODS

We carried out a case-control study with a cohort of participants enrolled during the first or early second trimester in the Center of Chiba Unit of the Japan Environment and Children's Study. Participants were classified as either gestational diabetes mellitus cases or matched controls based on age, body mass index and parity. Metabolite levels of their serum and urine obtained randomly before the diagnosis of gestational diabetes mellitus were analyzed using hydrophilic interaction chromatography tandem mass spectrometry. Orthogonal projections to latent structures discriminant analysis was carried out to investigate metabolome profiles for the different groups. Metabolites with a variable importance in projection value of >1.5 were identified as potential markers.

RESULTS

In total, 242 participants were enrolled in the study, of which 121 were cases. The R2X, R2Y and Q2 parameters for the discrimination ability of the resulting models were 0.388, 0.492 and 0.45 for serum, and 0.454, 0.674 and 0.483 for urine, respectively. We finally identified three metabolites in serum and 20 in urine as potential biomarkers. Glutamine in serum and ethanolamine and 1,3-diphosphoglycerate in urine showed >0.8 area under the receiver operating characteristic curves.

CONCLUSIONS

The present study identified serum and urine metabolites that are possible predictive markers of subsequent gestational diabetes mellitus in Japanese women. Further studies are required to elucidate their efficacy.

Identification of amniotic fluid metabolomic and placental transcriptomic changes associated with abnormal development of cloned pig fetuses

Piglets cloned by somatic cell nuclear transfer (SCNT) show a high incidence of malformations and a high death rate during the perinatal period. To investigate the underlying mechanisms for abnormal development of cloned pig fetuses, we compared body weight, amniotic fluid (AF) metabolome, and placental transcriptome between SCNT- and artificial insemination (AI)-derived pig fetuses. Results showed that the body weight of SCNT pig fetuses was significantly lower than that of AI pig fetuses. The identified differential metabolites between the two groups of AF were mainly involved in bile acids and steroid hormones. The levels of all detected bile acids in SCNT AF were significantly higher than those in AI AF. The increase in the AF bile acid levels in SCNT fetuses was linked with the downregulation of placental bile acid transporter expression and the abnormal development of placental folds (PFs), both of which negatively affected the transfer of bile acids from AF across the placenta into the mother's circulation. Alteration in the AF steroid hormone levels in cloned fetuses was associated with decreased expression of enzymes responsible for steroid hormone biosynthesis in the placenta. In conclusion, cloned pig fetuses undergo abnormal intrauterine development associated with alteration of bile acid and steroid hormone levels in AF, which may be due to the poor development of PFs and the erroneous expression of bile acid transporters and enzymes responsible for steroid hormone biosynthesis in the placentas.

Untargeted metabolomics: an overview of its usefulness and future potential in prenatal diagnosis

INTRODUCTION

Metabolomics opens up new avenues for biomarker discovery in different branches of medicine, including perinatology. Chromosomal aberration, preterm delivery (PTD), congenital heart defects, spina bifida, chorioamnionitis, and low birth weight are the main perinatal pathologies. Investigations using untargeted metabolomics have found the candidate metabolites for diagnostic biomarkers. Areas covered: This review describes areas of prenatal diagnosis in which untargeted metabolomics has been used. Data on the disease, type of sample, techniques used, number of samples used in the study, and metabolites obtained including the sign of their regulation are summarized. Expert commentary: Untargeted metabolomics is a powerful tool which can shed a new light on prenatal diagnostics. It helps to discover affected metabolic pathways what may help to reveal disease pathogenesis and propose potential biomarkers. Among others, glycerol and 2- and 3-hydroxybutyrate were proposed as markers of chromosomal aberration. Serum metabolic signature of PTD was characterized by increased lipids and decreased levels of hypoxanthine, tryptophane, and pyroglutamic acid. Lower level lipids and vitamin D3 metabolites together with increased bilirubin level in maternal serum were associated with macrosomia. However, to give a real value to those assays and allow their clinical application multicenter, large cohort validation studies are necessary.

Metabolomics of the amniotic fluid: Is it a feasible approach to evaluate the safety of Chinese medicine during pregnancy?

The use of Chinese medicines (CMs) during pregnancy has long been a major public health concern. Although CMs have been shown to be effective in treating infertility and preventing miscarriage, their use has been restricted, mainly because of limited knowledge of their potential toxicity. Accurate toxicology data are urgently required to assess whether these CMs are safe for maternal health and fetal development. Amniotic fluid (AF) contains carbohydrates, lipids and phospholipids, urea and proteins, all of which aid in the growth of the fetus and reflect the mother's health status as well. The changes in metabolomic patterns of AF are related to pathophysiological occurrences during the course of pregnancy. In this review, we provide a summary of the research performed in recent years on metabolomic AF samples, and use our previous study as an example to explore the feasibility of metabolomics of AF to evaluate the safety of CMs during pregnancy. We believe that metabolomics of AF play a far more important role than traditional morphology methods in the safety evaluation of CMs for pregnancy, with a higher sensitivity and correlation.

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

BACKGROUND

Gestational diabetes mellitus (GDM) reflects an increased risk of developing type 2 diabetes (T2D) after pregnancy in women. Offspring born to mothers with GDM are at an elevated risk of obesity and T2D at a young age. Currently, there are lack of ways for identifying women in early pregnancy who are at risk of developing GDM. As a result, both mothers and fetus are not treated until late in the second trimester when GDM is diagnosed. The recent advance in metabolomics, a new approach of systematic investigation of the metabolites, provides an opportunity for early detection of GDM, and classifying the risk of subsequent chronic diseases among women and their offspring.

METHODS

We reviewed the literatures published in the past 20 years on studies using high-throughput metabolomics technologies to investigate women with GDM and their offspring.

CONCLUSIONS

Despite the inconsistent results, previous studies have identified biomarkers that involved in specific metabolite groups and several pathways, including amino acid metabolism, steroid hormone biosynthesis, glycerophospholipid metabolism, and fatty acid metabolism. However, most studies have small sample sizes. Further research is warranted to determine if metabolomics will result in new indicators for the diagnosis, management, and prognosis of GDM and related complications.

1H NMR-based metabolomics reveals the effect of maternal habitual dietary patterns on human amniotic fluid profile

Maternal diet may influence offspring’s health, even within well-nourished populations. Amniotic fluid (AF) provides a rational compartment for studies on fetal metabolism. Evidence in animal models indicates that maternal diet affects AF metabolic profile; however, data from human studies are scarce. Therefore, we have explored whether AF content may be influenced by maternal diet, using a validated food-frequency questionnaire and implementing NMR-based metabolomics. Sixty-five AF specimens, from women undergoing second-trimester amniocentesis for prenatal diagnosis, were analysed. Complementary, maternal serum and urine samples were profiled. Hierarchical cluster analysis identified 2 dietary patterns, cluster 1 (C1, n = 33) and cluster 2 (C2, n = 32). C1 was characterized by significantly higher percentages of energy derived from refined cereals, yellow cheese, red meat, poultry, and “ready-to-eat” foods, while C2 by higher (P < 0.05) whole cereals, vegetables, fruits, legumes, and nuts. ¹H NMR spectra allowed the identification of metabolites associated with these dietary patterns; glucose, alanine, tyrosine, valine, citrate, cis-acotinate, and formate were the key discriminatory metabolites elevated in C1 AF specimens. This is the first evidence to suggest that the composition of AF is influenced by maternal habitual dietary patterns. Our results highlight the need to broaden the knowledge on the importance of maternal nutrition during pregnancy.

Maternal Plasma Metabolomic Profiles in Spontaneous Preterm Birth: Preliminary Results

Objective

To profile maternal plasma metabolome in spontaneous preterm birth.

Method

In this retrospective case-control study, we have examined plasma of patient with preterm birth (between 22 and 36 weeks of pregnancy (n = 57)), with threatened preterm labor (between 23 and 36 weeks of pregnancy (n = 49)), and with term delivery (n = 25). Plasma samples were analysed using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS) in positive and negative polarity modes.

Results

We found 168 differentially expressed metabolites that were significantly distinct between study groups. We determined 51 metabolites using publicly available databases that could be subdivided into one of the five groups: amino acids, fatty acids, lipids, hormones, and bile acids. PLS-DA models, verified by SVM classification accuracy, differentiated preterm birth and term delivery groups.

Conclusions

Maternal plasma metabolites are different between term and preterm parturitions. Part of them may be related with preterm labor, while others may be affected by gestational age or the beginning of labor. Metabolite profile can classify preterm or term delivery groups raising the potential of metabolome as a biomarker to identify high-risk pregnancies. Metabolomic studies are also a tool to detect individual compounds that may be further tested in targeted researches.

Biofluid Metabolomics in Preterm Birth Research

This article presents an account of the research carried out so far in the use of metabolomics to find biomarkers of preterm birth (PTB) in fetal, maternal, and newborn biofluids. Metabolomic studies have employed mainly nuclear magnetic resonance spectroscopy or mass spectrometry-based methodologies to analyze, on one hand, prenatal biofluids (amniotic fluid, maternal urine/maternal blood, cervicovaginal fluid) to identify predictive biomarkers of PTB, and on the other hand, biofluids collected at or after birth (amniotic fluid, umbilical cord blood, newborn urine, and newborn blood, maternal blood, or breast milk) to assess and follow up the health status of PTB babies. Besides advancing on the biochemical knowledge of PTB metabolism mainly during the in utero period and at birth, the work carried out has also helped to identify important requirements related to experimental design and analytical protocol that need to be addressed, if translation of these biomarkers to the clinic is to be envisaged. An outlook of possible future developments for the translation of laboratory results to the clinic is presented.

Translational Systems Pharmacology Studies in Pregnant Women

Pregnancy involves rapid physiological adaptation and complex interplay between mother and fetus. New analytic technologies provide large amounts of genomic, proteomic, and metabolomics data. The integration of these data through bioinformatics, statistical, and systems pharmacology techniques can improve our understanding of the mechanisms of normal maternal physiologic changes and fetal development. New insights into the mechanisms of pregnancy‐related disorders, such as preterm birth (PTB), may lead to the development of new therapeutic interventions and novel biomarkers.

Application of the amniotic fluid metabolome to the study of fetal malformations, using Down syndrome as a specific model

Although monitoring and diagnosis of fetal diseases in utero remains a challenge, metabolomics may provide an additional tool to study the etiology and pathophysiology of fetal diseases at a functional level. In order to explore specific markers of fetal disease, metabolites were analyzed in two separate sets of experiments using amniotic fluid from fetuses with Down syndrome (DS) as a model. Both sets included 10–15 pairs of controls and cases, and amniotic fluid samples were processed separately; metabolomic fingerprinting was then conducted using UPLC-MS. Significantly altered metabolites involved in respective metabolic pathways were compared in the two experimental sets. In addition, significantly altered metabolic pathways were further compared with the genomic characters of the DS fetuses. The data suggested that metabolic profiles varied across different experiments, however alterations in the 4 metabolic pathways of the porphyrin metabolism, bile acid metabolism, hormone metabolism and amino acid metabolism, were validated for the two experimental sets. Significant changes in metabolites of coproporphyrin III, glycocholic acid, taurochenodeoxycholate, taurocholate, hydrocortisone, pregnenolone sulfate, L-histidine, L-arginine, L-glutamate and L-glutamine were further confirmed. Analysis of these metabolic alterations was linked to aberrant gene expression at chromosome 21 of the DS fetus. The decrease in coproporphyrin III in the DS fetus may portend abnormal erythropoiesis, and unbalanced glutamine-glutamate concentration was observed to be closely associated with abnormal brain development in the DS fetus. Therefore, alterations in amniotic fluid metabolites may provide important clues to understanding the etiology of fetal disease and help to develop diagnostic testing for clinical applications.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

Metabolomics of Diabetes in Pregnancy

PURPOSE OF REVIEW

The purpose of this review is to describe ways in which metabolomics may enhance understanding of gestational diabetes mellitus (GDM) etiology and refine current diagnostic criteria.

RECENT FINDINGS

Current clinical recommendations suggest screening for GDM between 24 and 28 of gestational weeks using an oral glucose tolerance test. Despite this consensus, there are discrepancies regarding the exact criteria for GDM diagnosis. Further, emerging evidence has unveiled heterogeneous physiological pathways underlying GDM-specifically, GDM with defective insulin secretion vs. sensitivity-that have important implications for disease diagnosis and management. The objectives of this review are threefold. First, we seek to provide a brief summary of current knowledge regarding GDM pathophysiology. Next, we describe the potential role of metabolomics to refine and improve the prediction, screening, and diagnosis of GDM. Finally, we propose ways in which metabolomics may eventually impact clinical care and risk assessment for GDM and its comorbidities.

Tryptophan and purine metabolites are consistently upregulated in the urinary metabolome of patients diagnosed with gestational diabetes mellitus throughout pregnancy: A longitudinal metabolomics study of Chinese pregnant women part 2

BACKGROUND

Gestational diabetes mellitus (GDM) is a pathological state of glucose intolerance associated with adverse pregnancy outcomes and an increased risk of developing maternal type 2 diabetes later in life. The mechanisms underlying GDM development are not fully understood. We examined the pathophysiology of GDM through comprehensive metabolic profiling of maternal urine, using participants from a longitudinal cohort of normal pregnancies and pregnancies complicated by GDM.

METHODS

Based on ultra-performance liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry, an untargeted metabolomics study was performed to explore the differences in the urinary metabolome of GDM cases and healthy controls over the course of pregnancy. Multilevel statistical approaches were employed to address the complex metabolomic data obtained from a longitudinal cohort.

RESULTS

The results indicated that tryptophan and purine metabolism was associated with GDM. The tryptophan-kynurenine pathway was activated in the GDM subjects before placental hormones or the fetoplacental unit could have produced any physiological effect. Hypoxanthine, xanthine, xanthosine, and 1-methylhypoxanthine were all elevated in the urine metabolome of subjects with GDM. Catabolism of purine nucleosides leads ultimately to the production of uric acid, which discriminated the subjects with GDM from controls.

CONCLUSIONS

The results support the notion that GDM may be a predisposed condition, or prediabetic state, which is manifested during pregnancy. This challenges the conventional view of the pathogenesis of GDM, which assumes placental hormones are the major causes of insulin resistance in GDM.

Amniotic Fluid and Maternal Serum Metabolic Signatures in the Second Trimester Associated with Preterm Delivery

Preterm delivery (PTD) represents a major health problem that occurs in 1 in 10 births. The hypothesis of the present study was that the metabolic profile of different biological fluids, obtained from pregnant women during the second trimester of gestation, could allow useful correlations with pregnancy outcome. Holistic and targeted metabolomics approaches were applied for the complementary assessment of the metabolic content of prospectively collected amniotic fluid (AF) and paired maternal blood serum samples from 35 women who delivered preterm (between 29 weeks + 0 days and 36 weeks +5 days gestation) and 35 women delivered at term. The results revealed trends relating the metabolic content of the analyzed samples with preterm delivery. Untargeted and targeted profiling showed differentiations in certain key metabolites in the biological fluids of the two study groups. In AF, intermediate metabolites involved in energy metabolism (pyruvic acid, glutamic acid, and glutamine) were found to contribute to the classification of the two groups. In maternal serum, increased levels of lipids and alterations of key end-point metabolites were observed in cases of preterm delivery. Overall, the metabolic content of second-trimester AF and maternal blood serum shows potential for the identification of biomarkers related to fetal growth and preterm delivery.

Untargeted Metabolomic Analysis of Amniotic Fluid in the Prediction of Preterm Delivery and Bronchopulmonary Dysplasia

Objective

Bronchopulmonary dysplasia (BPD) is a serious complication associated with preterm birth. A growing body of evidence suggests a role for prenatal factors in its pathogenesis. Metabolomics allows simultaneous characterization of low molecular weight compounds and may provide a picture of such a complex condition. The aim of this study was to evaluate whether an unbiased metabolomic analysis of amniotic fluid (AF) can be used to investigate the risk of spontaneous preterm delivery (PTD) and BPD development in the offspring.

Study design

We conducted an exploratory study on 32 infants born from mothers who had undergone an amniocentesis between 21 and 28 gestational weeks because of spontaneous preterm labor with intact membranes. The AF samples underwent untargeted metabolomic analysis using mass spectrometry combined with ultra-performance liquid chromatography. The data obtained were analyzed using multivariate and univariate statistical data analysis tools.

Results

Orthogonally Constrained Projection to Latent Structures-Discriminant Analysis (oCPLS2-DA) excluded effects on data modelling of crucial clinical variables. oCPLS2-DA was able to find unique differences in select metabolites between term (n = 11) and preterm (n = 13) deliveries (negative ionization data set: R² = 0.47, mean AUC ROC in prediction = 0.65; positive ionization data set: R² = 0.47, mean AUC ROC in prediction = 0.70), and between PTD followed by the development of BPD (n = 10), and PTD without BPD (n = 11) (negative data set: R² = 0.48, mean AUC ROC in prediction = 0.73; positive data set: R² = 0.55, mean AUC ROC in prediction = 0.71).

Conclusions

This study suggests that amniotic fluid metabolic profiling may be promising for identifying spontaneous preterm birth and fetuses at risk for developing BPD. These findings support the hypothesis that some prenatal metabolic dysregulations may play a key role in the pathogenesis of PTD and the development of BPD.

Current LC-MS methods and procedures applied to the identification of new steroid metabolites

The study of the metabolism of steroids has a long history; from the first characterizations of the major metabolites of steroidal hormones in the pre-chromatographic era, to the latest discoveries of new forms of excretions. The introduction of mass spectrometers coupled to gas chromatography at the end of the 1960's represented a major breakthrough for the elucidation of new metabolites. In the last two decades, this technique is being complemented by the use of liquid chromatography-mass spectrometry (LC-MS). In addition of becoming fundamental in clinical steroid determinations due to its excellent specificity, throughput and sensitivity, LC-MS has emerged as an exceptional tool for the discovery of new steroid metabolites. The aim of the present review is to provide an overview of the current LC-MS procedures used in the quest of novel metabolic products of steroidal hormones and exogenous steroids. Several aspects regarding LC separations are first outlined, followed by a description of the key processes that take place in the mass spectrometric analysis, i.e. the ionization of the steroids in the source and the fragmentation of the selected precursor ions in the collision cell. The different analyzers and approaches employed together with representative examples of each of them are described. Special emphasis is placed on triple quadrupole analyzers (LC-MS/MS), since they are the most commonly employed. Examples on the use of precursor ion scan, neutral loss scan and theoretical selected reaction monitoring strategies are also explained.

Development of a Pipeline for Exploratory Metabolic Profiling of Infant Urine

Numerous metabolic profiling pipelines have been developed to characterize the composition of human biofluids and tissues, the vast majority of these being for studies in adults. To accommodate limited sample volume and to take into account the compositional differences between adult and infant biofluids, we developed and optimized sample handling and analytical procedures for studying urine from newborns. A robust pipeline for metabolic profiling using NMR spectroscopy was established, encompassing sample collection, preparation, spectroscopic measurement, and computational analysis. Longitudinal samples were collected from five infants from birth until 14 months of age. Methods of extraction and effects of freezing and sample dilution were assessed, and urinary contaminants from breakdown of polymers in a range of diapers and cotton wool balls were identified and compared, including propylene glycol, acrylic acid, and tert-butanol. Finally, assessment of urinary profiles obtained over the first few weeks of life revealed a dramatic change in composition, with concentrations of phenols, amino acids, and betaine altering systematically over the first few months of life. Therefore, neonatal samples require more stringent standardization of experimental design, sample handling, and analysis compared to that of adult samples to accommodate the variability and limited sample volume.

The choice of amniotic fluid in metabolomics for the monitoring of fetus health

Amniotic fluid (AF) is a biological fluid in which metabolite transport is regulated by the placenta, the permeable skin, fetal lung egress and gastric fluid. During pregnancy, the composition of AF changes from similar to the interstitial fluid of the mother, to a more complex system, influenced by the fetus's urine. Since AF reflects the mother's and the fetus's health status at the same time, it may be an important diagnostic tool for a wider spectrum of clinical conditions. Indeed, the metabolic characterization of AF in relation to pathological occurrences may lead to the discovery of new biomarkers for a better clinical practice. For this reason, metabolomics may be the most suitable strategy for this task. In this review, research works on metabolomic AF analysis are discussed according to the morbidity of interest, being preterm birth/labor, gestational age and diabetes and fetal malformations, along with a number of other important studies.

High-Resolution Metabolomics: Review of the Field and Implications for Nursing Science and the Study of Preterm Birth

Most complex health conditions do not have a single etiology but rather develop from exposure to multiple risk factors that interact to influence individual susceptibility. In this review, we discuss the emerging field of metabolomics as a means by which metabolic pathways underlying a disease etiology can be exposed and specific metabolites can be identified and linked, ultimately providing biomarkers for early detection of disease onset and new strategies for intervention. We present the theoretical foundation of metabolomics research, the current methods employed in its conduct, and the overlap of metabolomics research with other "omic" approaches. As an exemplar, we discuss the potential of metabolomics research in the context of deciphering the complex interactions of the maternal-fetal exposures that underlie the risk of preterm birth, a condition that accounts for substantial portions of infant morbidity and mortality and whose etiology and pathophysiology remain incompletely defined. We conclude by providing strategies for including metabolomics research in future nursing studies for the advancement of nursing science.

Metabolomics in Newborns

Metabolomics is the quantitative analysis of a large number of low molecular weight metabolites that are intermediate or final products of all the metabolic pathways in a living organism. Any metabolic profiles detectable in a human biological fluid are caused by the interaction between gene expression and the environment. The metabolomics approach offers the possibility to identify variations in metabolite profile that can be used to discriminate disease. This is particularly important for neonatal and pediatric studies especially for severe ill patient diagnosis and early identification. This property is of a great clinical importance in view of the newer definitions of health and disease. This review emphasizes the workflow of a typical metabolomics study and summarizes the latest results obtained in neonatal studies with particular interest in prematurity, intrauterine growth retardation, inborn errors of metabolism, perinatal asphyxia, sepsis, necrotizing enterocolitis, kidney disease, bronchopulmonary dysplasia, and cardiac malformation and dysfunction.

Impact of fetal chromosomal disorders on maternal blood metabolome: toward new biomarkers?

OBJECTIVE

This study aimed at determining the relationship between fetal chromosomal disorders (CDs), including trisomy 21 (T21), and on first- and second-trimester maternal blood plasma, to identify the time-course metabolic adaptations to the conditions and the possible new plasma biomarkers. Furthermore, a definition of a joint circulatory (plasma) and excretory (urine) metabolic description of second-trimester CDs was sought.

STUDY DESIGN

Plasma was obtained for 119 pregnant women: 74 controls and 45 CD cases, including 22 T21 cases. Plasma and lipid extracts (for T21 only) were analyzed by nuclear magnetic resonance spectroscopy, and data were handled by variable selection and multivariate analysis. Correlation analysis was used on a concatenated plasma/urine matrix descriptive of second-trimester CD, based on previously obtained urine data.

RESULTS

CD cases were accompanied by enhanced lipid β-oxidation (increased ketone bodies) and underutilization of glucose, pyruvate, and citrate. Lower circulating high-density lipoprotein levels were noted, along with changes in the proline and methanol in the first trimester, and also the urea, creatinine, acetate, and low-density lipoprotein plus very low-density lipoprotein in the second trimester and the different urea and creatinine levels, suggesting fetal renal dysfunction. In terms of plasma composition, T21 cases were indistinguishable from other CDs in the first trimester, whereas in the second trimester, increased methanol and albumin may be T21 specific. Furthermore, first-trimester lipid extracts of T21 showed decreased levels of 18:2 fatty acids, whereas in the second trimester, lower levels of 20:4 and 22:6 fatty acids were noted, possibly indicative of inflammation mechanisms. In both trimesters, high classification rates for CDs (88-89%) and T21 (85-92%) generally relied on variable selection of nuclear magnetic resonance data. Plasma/urine correlations confirmed most metabolic deviations and unveiled possible new ones regarding low-density lipoprotein plus very low-density lipoprotein, sugar, and gut-microflora metabolisms.

CONCLUSION

This work partially confirmed previously reported data on first-trimester T21 and provided additional information on time-course metabolic changes accompanying CD and T21, in particular regarding plasma lipid composition. These results demonstrate the potential of plasma metabolomics in monitoring and characterizing CD cases; however, validation in larger cohorts is desirable.