Applications of Metabolomics in the Study and Management of Preeclampsia; A Review of the Literature

Metabolomic prediction of severe maternal and newborn complications in preeclampsia

INTRODUCTION

Preeclampsia (PreE) remains a major source of maternal and newborn complications. Prenatal prediction of these complications could significantly improve pregnancy management.

OBJECTIVES

Using metabolomic analysis we investigated the prenatal prediction of maternal and newborn complications in early and late PreE and investigated the pathogenesis of such complications.

METHODS

Serum samples from 76 cases of PreE (36 early-onset and 40 late-onset), and 40 unaffected controls were collected. Direct Injection Liquid Chromatography-Mass Spectrometry combined with Nuclear Magnetic Resonance (NMR) spectroscopy was performed. Logistic regression analysis was used to generate models for prediction of adverse maternal and neonatal outcomes in patients with PreE. Metabolite set enrichment analysis (MSEA) was used to identify the most dysregulated metabolites and pathways in PreE.

RESULTS

Forty-three metabolites were significantly altered (p < 0.05) in PreE cases with maternal complications and 162 metabolites were altered in PreE cases with newborn adverse outcomes. The top metabolite prediction model achieved an area under the receiver operating characteristic curve (AUC) = 0.806 (0.660-0.952) for predicting adverse maternal outcomes in early-onset PreE, while the AUC for late-onset PreE was 0.843 (0.712-0.974). For the prediction of adverse newborn outcomes, regression models achieved an AUC = 0.828 (0.674-0.982) in early-onset PreE and 0.911 (0.828-0.994) in late-onset PreE. Profound alterations of lipid metabolism were associated with adverse outcomes.

CONCLUSION

Prenatal metabolomic markers achieved robust prediction, superior to conventional markers for the prediction of adverse maternal and newborn outcomes in patients with PreE. We report for the first-time the prediction and metabolomic basis of adverse maternal and newborn outcomes in patients with PreE.

Relationship between the concentration of ergothioneine in plasma and the likelihood of developing pre-eclampsia

Ergothioneine, an antioxidant nutraceutical mainly at present derived from the dietary intake of mushrooms, has been suggested as a preventive for pre-eclampsia (PE). We analysed early pregnancy samples from a cohort of 432 first time mothers as part of the Screening for Endpoints in Pregnancy (SCOPE, European branch) project to determine the concentration of ergothioneine in their plasma. There was a weak association between the ergothioneine levels and maternal age but none for BMI. Of these 432 women, 97 went on to develop pre-term (23) or term (74) PE. If a threshold was set at the 90th percentile of the reference range in the control population (≥462 ng/ml), only one of these 97 women (1%) developed PE, versus 96/397 (24.2%) whose ergothioneine level was below this threshold. One possible interpretation of these findings, consistent with previous experiments in a reduced uterine perfusion model in rats, is that ergothioneine may indeed prove protective against PE in humans. An intervention study of some kind now seems warranted.

NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples

Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. Besides, the lack of fast and accurate computational tools that can handle the automatic identification and quantification of essential metabolites from NMR spectra also slows the wide application of these techniques in clinical. We present NMRQNet, a deep-learning-based pipeline for automatic identification and quantification of dominant metabolite candidates within human plasma samples. The estimated relative concentrations could be further applied in statistical analysis to extract the potential biomarkers. We evaluate our method on multiple plasma samples, including species from mice to humans, curated using three anticoagulants, covering healthy and patient conditions in neurological disorder disease, greatly expanding the metabolomics analytical space in plasma. NMRQNet accurately reconstructed the original spectra and obtained significantly better quantification results than the earlier computational methods. Besides, NMRQNet also proposed relevant metabolites biomarkers that could potentially explain the risk factors associated with the condition. NMRQNet, with improved prediction performance, highlights the limitations in the existing approaches and has shown strong application potential for future metabolomics disease studies using plasma samples.

SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra

Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

Effect of common pregnancy and perinatal complications on offspring metabolic traits across the life course: a multi-cohort study

BACKGROUND

Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age.

METHODS

We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1-1.6 years), childhood (4.2-7.5 years); adolescence (12.0-16.0 years), and adulthood (22.0-67.8 years).

RESULTS

Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of -0.89 standard deviation (SD) units for albumin with PTB (95% CI: -1.10 to -0.69, P=1.3×10-17) and -0.41 SD for total lipids in medium HDL with SGA (95% CI: -0.56 to -0.25, P=2.6×10-7), with some evidence of persistence to older ages. LGA was inversely associated with 19 metabolic traits including lower levels of cholesterol, lipoproteins, fatty acids, and amino acids, with associations emerging in adolescence, (e.g. -0.11 SD total fatty acids, 95% CI: -0.18 to -0.05, P=0.0009), and attenuating with older age across adulthood.

CONCLUSIONS

These reassuring findings suggest little evidence of wide-spread and long-term impact of common pregnancy and perinatal complications on offspring metabolic traits, with most associations only observed for newborns rather than older ages, and for perinatal rather than pregnancy complications.

Significance of analysis of lipid extracts in cervical canal secretion for diagnosing of placenta-associated complications of pregnancy

Omics technologies hold great potential as the basis for development of the new diagnostic approaches in obstetrics. Cervicovaginal fluid (CVF) as part of the mother-placenta-fetus system can be used to diagnose obstetric complications. This study aimed to identify the features of lipid composition of the cervical canal secretion peculiar to Intrauterine Growth Restriction (IUGR) and preeclampsia (PE). We took CVF samples from 57 pregnant women and subjected them to an in-depth clinical-anamnestic and mass-spectrometric analysis. Lipid extracts of CVF were analyzed with a liquid chromatography system coupled with a mass analyzer. As a result, we identified 239 lipid compounds. In case of 17 lipids, mathematical analysis revealed significant differences between samples from women with normal pregnancy indicator values (normal group) and patients from the IUGR group (p < 0.05). As for the normal group and PE group patients, there were significant differences identified for 3 lipids (p < 0.05). Comparison of samples from the PE and IUGR groups yielded statistically significant differences in levels of two lipids (p < 0.05). Mainly, the lipids were oxylipins, sphingomyelins, triglycerides, and cardiolipins. The developed diagnostic model had the sensitivity of 0.81 and specificity of 0.91 (cut-off level — 0.50; AUC — 0.85). The data obtained are valuable in the context of development of the new methods of diagnosing placentaassociated complications of pregnancy and for understanding new mechanisms of pathogenesis of these complications.

Early-pregnancy prediction of risk for pre-eclampsia using maternal blood leptin/ceramide ratio: discovery and confirmation

OBJECTIVE

This study aimed to develop a blood test for the prediction of pre-eclampsia (PE) early in gestation. We hypothesised that the longitudinal measurements of circulating adipokines and sphingolipids in maternal serum over the course of pregnancy could identify novel prognostic biomarkers that are predictive of impending event of PE early in gestation.

STUDY DESIGN

Retrospective discovery and longitudinal confirmation.

SETTING

Maternity units from two US hospitals.

PARTICIPANTS

Six previously published studies of placental tissue (78 PE and 95 non-PE) were compiled for genomic discovery, maternal sera from 15 women (7 non-PE and 8 PE) enrolled at ProMedDx were used for sphingolipidomic discovery, and maternal sera from 40 women (20 non-PE and 20 PE) enrolled at Stanford University were used for longitudinal observation.

OUTCOME MEASURES

Biomarker candidates from discovery were longitudinally confirmed and compared in parallel to the ratio of placental growth factor (PlGF) and soluble fms-like tyrosine kinase (sFlt-1) using the same cohort. The datasets were generated by enzyme-linked immunosorbent and liquid chromatography-tandem mass spectrometric assays.

RESULTS

Our discovery integrating genomic and sphingolipidomic analysis identified leptin (Lep) and ceramide (Cer) (d18:1/25:0) as novel biomarkers for early gestational assessment of PE. Our longitudinal observation revealed a marked elevation of Lep/Cer (d18:1/25:0) ratio in maternal serum at a median of 23 weeks' gestation among women with impending PE as compared with women with uncomplicated pregnancy. The Lep/Cer (d18:1/25:0) ratio significantly outperformed the established sFlt-1/PlGF ratio in predicting impending event of PE with superior sensitivity (85% vs 20%) and area under curve (0.92 vs 0.52) from 5 to 25 weeks of gestation.

CONCLUSIONS

Our study demonstrated the longitudinal measurement of maternal Lep/Cer (d18:1/25:0) ratio allows the non-invasive assessment of PE to identify pregnancy at high risk in early gestation, outperforming the established sFlt-1/PlGF ratio test.

Metabolomics to understand placental biology: Where are we now?

Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Circulating Plasma Metabolites and Cognitive Function in a Puerto Rican Cohort

BACKGROUND

Minorities, including mainland Puerto Ricans, are impacted disproportionally by Alzheimer's disease (AD), dementia, and cognitive decline. Studying blood metabolomics in this population has the potential to probe the biological underpinnings of this health disparity.

OBJECTIVE

We performed a comprehensive analysis of circulating plasma metabolites in relation to cognitive function in 736 participants from the Boston Puerto Rican Health Study (BPRHS) who underwent untargeted mass-spectrometry based metabolomics analysis and had undergone a battery of in-person cognitive testing at baseline.

METHODS

After relevant exclusions, 621 metabolites were examined. We used multivariable regression, adjusted for age, sex, education, apolipoprotein E genotype, smoking, and Mediterranean dietary pattern, to identify metabolites related to global cognitive function in our cohort. LASSO machine learning was used in a complementary analysis to identify metabolites that could discriminate good from poor extremes of cognition. We also conducted sensitivity analyses: restricted to participants without diabetes, and to participants with good adherence to Mediterranean diet.

RESULTS

Of 621 metabolites, FDR corrected (p < 0.05) multivariable linear regression identified 3 metabolites positively, and 10 negatively, associated with cognitive function in the BPRHS. In a combination of FDR-corrected linear regression, logistic regression regularized via LASSO, and sensitivity analyses restricted to participants without diabetes, and with good adherence to the Mediterranean diet, β-cryptoxanthin plasma concentration was consistently associated with better cognitive function and N-acetylisoleucine and tyramine O-sulfate concentrations were consistently associated with worse cognitive function.

CONCLUSION

This untargeted metabolomics study identified potential biomarkers for cognitive function in a cohort of Puerto Rican older adults.

Serum from patients with hypertension promotes endothelial dysfunction to induce trophoblast invasion through the miR‑27b‑3p/ATPase plasma membrane Ca2+ transporting 1 axis

Pregnancy‑induced hypertension is often accompanied by preeclampsia. The present study investigated whether microRNA (miR)‑27b‑3p affected the occurrence of preeclampsia by regulating the function of endothelial cells. Expressions levels of miR‑27b‑3p and ATPase plasma membrane Ca²⁺ transporting 1 (ATP2B1) were determined using reverse‑transcription quantitative PCR. miR‑27b‑3p targeting ATP2B1 was predicted using bioinformatics and further confirmed by dual‑luciferase reporter assays. Cell Counting Kit‑8, Transwell and Matrigel tube formation assays were performed to detect the effects of miR‑27b‑3p on proliferation, migration and tube formation of human umbilical vein endothelial cells (HUVECs), respectively. Moreover, HTR8/SVneos cells were co‑cultured with HUVECs to detect the invasion of trophoblast cells, and the expression levels of vascular endothelial growth factor (VEGF), matrix metalloproteinase (MMP)‑2 and MMP‑9 of HUVECs and HTR8/SVneos were detected by western blotting. Expression levels of miR‑27b‑3p were upregulated in the serum of patients with hypertension and preeclampsia, which could target and regulate the expression of ATP2B1. The expression levels of miR‑27b‑3p were increased and those of ATP2B1 were reduced in HUVECs from hypertensive serums. Moreover, miR‑27b‑3p mimics reduced the expression level of ATP2B1, and miR‑27b‑3p inhibitor reversed the effect of hypertensive serum on ATP2B1 expression. Furthermore, patients with hypertension showed increased endothelial dysfunction, reduced trophoblastic invasion and the expressions of VEGF, MMP‑2 and MMP‑9, and miR‑27b‑3p mimics and silencing of ATP2B1 produced similar results to HUVECs. The miR‑27b‑3p inhibitor reversed the effect of hypertensive serum, and silencing of ATP2B1 inhibited the improvement of miR‑27b‑3p inhibitor to HUVECs and HTR‑8/SVneo cells in proliferation, migration and tube formation. The current findings suggested that miR‑27b‑3p promoted proliferation, migration and tube formation of HUVECs and enhanced invasion of trophoblast cells, via regulation of ATP2B1. Thus, miR‑27b‑3p could be considered as a molecular risk factor in the pathogenesis and development of preeclampsia.

Untargeted analysis of first trimester serum to reveal biomarkers of pregnancy complications: a case-control discovery phase study

Understanding of causal biology and predictive biomarkers are lacking for hypertensive disorders of pregnancy (HDP) and preterm birth (PTB). First-trimester serum specimens from 51 cases of HDP, including 18 cases of pre-eclampsia (PE) and 33 cases of gestational hypertension (GH); 53 cases of PTB; and 109 controls were obtained from the Global Alliance to Prevent Prematurity and Stillbirth repository. Metabotyping was conducted using liquid chromatography high resolution mass spectroscopy and nuclear magnetic resonance spectroscopy. Multivariable logistic regression was used to identify signals that differed between groups after controlling for confounders. Signals important to predicting HDP and PTB were matched to an in-house physical standards library and public databases. Pathway analysis was conducted using GeneGo MetaCore. Over 400 signals for endogenous and exogenous metabolites that differentiated cases and controls were identified or annotated, and models that included these signals produced substantial improvements in predictive power beyond models that only included known risk factors. Perturbations of the aminoacyl-tRNA biosynthesis, L-threonine, and renal secretion of organic electrolytes pathways were associated with both HDP and PTB, while pathways related to cholesterol transport and metabolism were associated with HDP. This untargeted metabolomics analysis identified signals and common pathways associated with pregnancy complications.

Global metabolomic profiling reveals hepatic biosignatures that reflect the unique metabolic needs of late-term mother and fetus

OBJECTIVE

Gestational disorders including preeclampsia, growth restriction and diabetes are characterized, in part, by altered metabolic interactions between mother and fetus. Understanding their functional relevance requires metabolic characterization under normotypic conditions.

METHODS

We performed untargeted metabolomics on livers of pregnant, late-term C57Bl/6J mice (N = 9 dams) and their fetuses (pooling 4 fetuses/litter), using UPLC-MS/MS.

RESULTS

Multivariate analysis of 730 hepatic metabolites revealed that maternal and fetal metabolite profiles were highly compartmentalized, and were significantly more similar within fetuses (ρ_average = 0.81), or within dams (ρ_average = 0.79), than within each maternal-fetal dyad (ρ_average = - 0.76), suggesting that fetal hepatic metabolism is under distinct and equally tight metabolic control compared with its respective dam. The metabolite profiles were consistent with known differences in maternal-fetal metabolism. The reduced fetal glucose reflected its limited capacity for gluconeogenesis and dependence upon maternal plasma glucose pools. The fetal decreases in essential amino acids and elevations in their alpha-keto acid carnitine conjugates reflects their importance as secondary fuel sources to meet fetal energy demands. Whereas, contrasting elevations in fetal serine, glycine, aspartate, and glutamate reflects their contributions to endogenous nucleotide synthesis and fetal growth. Finally, the elevated maternal hepatic lipids and glycerol were consistent with a catabolic state that spares glucose to meet competing maternal-fetal energy demands.

CONCLUSIONS

The metabolite profile of the late-term mouse dam and fetus is consistent with prior, non-rodent analyses utilizing plasma and urine. These data position mouse as a suitable model for mechanistic investigation into how maternal-fetal metabolism adapts (or not) to gestational stressors.

The influence of pregnancy-induced hypertension syndrome on the metabolism of newborns

BACKGROUND

Pregnancy-induced hypertension (PIH) is associated with an increased number of neonatal complications, but its impact on neonatal metabolism remains unclear. This study aimed to investigate the differential metabolomics of infants born to mothers with and without PIH.

METHODS

Blood samples of a total of 115 infants born to mothers with (n=56) and without (n=59) PIH were collected and assigned to two groups, respectively, from the neonatal department of Sixth Affiliated Hospital of Sun Yat-Sen University. A tandem mass spectrometer was used to generate metabolic profiling of amino acid, free carnitine, and acyl-carnitines. The resulting data were analyzed using orthogonal partial least squares discriminant analysis based on the difference between infants born to mothers with or without PIH.

RESULTS

A significant relationship was observed between the two groups (with and without PIH) in the metabolic fingerprint. According to the pattern recognition analysis combined with variance importance, 25 metabolites with high importance were found. The top ten substances were selected for analysis. Compared with infants born to mothers without PIH, glycine levels increased, and C14DC, C22, C4DC, C5:1, C6DC, C5-OH, proline, C14-OH, and C20 decreased in infants born to mothers with PIH.

CONCLUSIONS

Using liquid chromatography (LC)-MS/MS metabolomics, a significant relationship was detected between neonatal metabolism and maternal hypertension. It is important to correct the subsequent infantile metabolic disorder by balancing the biomarker metabolites and suppling adequate nutrition to improve the health and growth of newborns of PIH mothers.

Metabolomic, hormonal and physiological responses to hypoglycemia versus euglycemia during exercise in adults with type 1 diabetes

INTRODUCTION

This study sought to compare the metabolomic, hormonal and physiological responses to hypoglycemia versus euglycemia during exercise in adults with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

Thirteen individuals with T1D (hemoglobin; 7.0%±1.3% (52.6±13.9 mmol/mol), age; 36±15 years, duration diabetes; 15±12 years) performed a maximum of 45 min submaximal exercise (60%±6% V̇O_2max). Retrospectively identified exercise sessions that ended in hypoglycemia ((HypoEx) blood glucose (BG)≤3.9 mmol/L) were compared against a participant-matched euglycemic condition ((EuEx) BG≥4.0, BG≤10.0 mmol/L). Samples were compared for detailed physiological and hormonal parameters as well as metabolically profiled via large scale targeted ultra-high-performance liquid chromatography coupled to tandem mass spectrometry. Data were assessed using univariate and multivariate analysis techniques with false discovery rate adjustment. Significant results were considered at p≤0.05.

RESULTS

Cardiorespiratory and counterregulatory hormone responses, whole-body fuel use and perception of fatigue during exercise were similar under conditions of hypoglycemia and euglycemia (BG 3.5±0.3 vs 5.8±1.1 mmol/L, respectively p<0.001). HypoEx was associated with greater adenosine salvage pathway activity (5'-methylthioadenosine, p=0.023 and higher cysteine and methionine metabolism), increased utilization of glucogenic amino acids (glutamine, p=0.021, alanine, aspartate and glutamate metabolism and homoserine/threonine, p=0.045) and evidence of enhanced β-oxidation (lower carnitine p<0.001, higher long-chain acylcarnitines).

CONCLUSIONS

Exposure to acute hypoglycemia during exercise potentiates alterations in subclinical indices of metabolic stress at the level of the metabolome. However, the physiological responses induced by dynamic physical exercise may mask the symptomatic recognition of mild hypoglycemia during exercise in people with T1D, a potential clinical safety concern that reinforces the need for diligent glucose management.

TRIAL REGISTRATION NUMBER

DRKS00013509.

Lipidomic profiling of chorionic villi in the placentas of women with chronic venous disease

Background: Chronic venous disease (CVD) is a prevalent lower limb venous pathology that especially affects women, who also show an increased risk of this disease during pregnancy. Studies have shown significant structural changes in the placentas of women with CVD and several markers of tissue damage have been also described. Patients and Methods: To try to understand the different placental pathologies, research efforts have focused on examining metabolomic profiles as indicators of the repercussions of these vascular disorders. This study examines changes produced in the metabolomic profiles of chorionic villi in the placentas of women with CVD. In a study population of 12 pregnant women, 6 with and 6 without CVD, we compared through mass spectroscopy coupled to ultra-high performance liquid chromatography (UHPLC-MS), 240 metabolites in chorionic villus samples. Results: This study is the first to detect in the placental villi of pregnant women with CVD, modifications in lysophosphatidylcholines and amino acids along with diminished levels of other lipids such as triglycerides, sphingomyelins, and non-esterified omega 9 fatty acids, suggesting a role of these abnormalities in the pathogenesis of CVD. Conclusions: Our findings are a starting point for future studies designed to examine the impacts of CVD on maternal and fetal well-being.

Metabolomics in the study of spontaneous animal diseases

Using analytical chemistry techniques such as nuclear magnetic resonance (NMR) spectroscopy and liquid or gas chromatography-mass spectrometry (LC/GC-MS), metabolomics allows detection of most endogenous and exogenous metabolites in a biological sample. Metabolomics has a wide range of applications, and has been employed in nutrition science, toxicology, environmental studies, and systems biology. Metabolomics is particularly useful in biomedical science, and has been used for diagnostic laboratory testing, identifying targets for drug development, and monitoring drug metabolism, mode of action, and toxicity. Despite its immense potential, metabolomics remains underutilized in the study of spontaneous animal diseases. Our aim was to comprehensively review the existing literature on the use of metabolomics in spontaneous veterinary diseases. Three databases were used to find journal articles that applied metabolomics in veterinary medicine. A screening process was then conducted to eliminate references that did not meet the eligibility criteria; only primary research studies investigating spontaneous animal disease were included; 38 studies met the inclusion criteria. The main techniques used were NMR and MS. All studies detected metabolite alterations in diseased animals compared with non-diseased animals. Metabolomics was mainly used to study diseases of the digestive, reproductive, and musculoskeletal systems. Inflammatory conditions made up the largest proportion of studies when articles were categorized by disease process. Following a comprehensive analysis of the literature on metabolomics in spontaneous veterinary diseases, we concluded that metabolomics, although in its early stages in veterinary research, is a promising tool regarding diagnosis, biomarker discovery, and in uncovering new insights into disease pathophysiology.

4-Hydroxyglutamate is a novel predictor of pre-eclampsia

Background

Pre-term pre-eclampsia is a major cause of maternal and perinatal morbidity and mortality worldwide. A multi-centre randomized–controlled trial has shown that first-trimester screening followed by treatment of high-risk women with aspirin reduces the risk of pre-term pre-eclampsia. However, the biomarkers currently employed in risk prediction are only weakly associated with the outcome.

Methods

We conducted a case–cohort study within the Pregnancy Outcome Prediction study to analyse untargeted maternal serum metabolomics in samples from 12, 20, 28 and 36 weeks of gestational age (wkGA) in women with pre-eclampsia delivering at term (n = 165) and pre-term (n = 29), plus a random sample of the cohort (n = 325). We used longitudinal linear mixed models to identify candidate metabolites at 20/28 wkGA that differed by term pre-eclampsia status. Candidates were validated using measurements at 36 wkGA in the same women. We then tested the association between the 12-, 20- and 28-wkGA measurements and pre-term pre-eclampsia. We externally validated the association using 24- to 28-wkGA samples from the Born in Bradford study (25 cases and 953 controls).

Results

We identified 100 metabolites that differed most at 20/28 wkGA in term pre-eclampsia. Thirty-three of these were validated (P < 0.0005) at 36 wkGA. 4-Hydroxyglutamate and C-glycosyltryptophan were independently predictive at 36 wkGA of term pre-eclampsia. 4-Hydroxyglutamate was also predictive (area under the receiver operating characteristic curve, 95% confidence interval) of pre-term pre-eclampsia at 12 (0.673, 0.558–0.787), 20 (0.731, 0.657–0.806) and 28 wkGA (0.733, 0.627–0.839). The predictive ability of 4-hydroxyglutamate at 12 wkGA was stronger than two existing protein biomarkers, namely PAPP-A (0.567, 0.439–0.695) and placenta growth factor (0.589, 0.463–0.714). Finally, 4-hydroxyglutamate at 24–28 wkGA was positively associated with pre-eclampsia (term or pre-term) among women from the Born in Bradford study.

Conclusions

4-hydroxyglutamate is a novel biochemical predictor of pre-eclampsia that provides better first-trimester prediction of pre-term disease than currently employed protein biomarkers.

Vaginal metabolome: towards a minimally invasive diagnosis of microbial invasion of the amniotic cavity in women with preterm labor

Microbial invasion of the amniotic cavity (MIAC) is only identified by amniocentesis, an invasive procedure that limits its clinical translation. Here, we aimed to evaluate whether the vaginal metabolome discriminates the presence/absence of MIAC in women with preterm labor (PTL) and intact membranes. We conducted a case-control study in women with symptoms of PTL below 34 weeks who underwent amniocentesis to discard MIAC. MIAC was defined as amniotic fluid positive for microorganisms identified by specific culture media. The cohort included 16 women with MIAC and 16 control (no MIAC). Both groups were matched for age and gestational age at admission. Vaginal fluid samples were collected shortly after amniocentesis. Metabolic profiles were analyzed by nuclear magnetic resonance (NMR) spectroscopy and compared using multivariate and univariate statistical analyses to identify significant differences between the two groups. The vaginal metabolomics profile of MIAC showed higher concentrations of hypoxanthine, proline, choline and acetylcholine and decreased concentrations of phenylalanine, glutamine, isoleucine, leucine and glycerophosphocholine. In conclusion, metabolic changes in the NMR-based vaginal metabolic profile are able to discriminate the presence/absence of MIAC in women with PTL and intact membranes. These metabolic changes might be indicative of enhanced glycolysis triggered by hypoxia conditions as a consequence of bacterial infection, thus explaining the utilization of alternative energy sources in an attempt to replenish glucose.

Metabolomic Pathways Predicting Labor Dystocia by Maternal Body Mass Index

Objectives  The purpose of this study was to evaluate the metabolic pathways activated in the serum of African-American women during late pregnancy that predicted term labor dystocia. Study Design  Matched case-control study ( n  = 97; 48 cases of term labor dystocia and 49 normal labor progression controls) with selection based on body mass index (BMI) at hospital admission and maternal age. Late pregnancy serum samples were analyzed using ultra-high-resolution metabolomics. Differentially expressed metabolic features and pathways between cases experiencing term labor dystocia and normal labor controls were evaluated in the total sample, among women who were obese at the time of labor (BMI ≥ 30 kg/m2), and among women who were not obese. Results  Labor dystocia was predicted by different metabolic pathways in late pregnancy serum among obese (androgen/estrogen biosynthesis) versus nonobese African-American women (fatty acid activation, steroid hormone biosynthesis, bile acid biosynthesis, glycosphingolipid metabolism). After adjusting for maternal BMI and age in the total sample, labor dystocia was predicted by tryptophan metabolic pathways in addition to C21 steroid hormone, glycosphingolipid, and androgen/estrogen metabolism. Conclusion  Metabolic pathways consistent with lipotoxicity, steroid hormone production, and tryptophan metabolism in late pregnancy serum were significantly associated with term labor dystocia in African-American women.

Differences in Pregnancy Metabolic Profiles and Their Determinants between White European and South Asian Women: Findings from the Born in Bradford Cohort

There is widespread metabolic disruption in women upon becoming pregnant. South Asians (SA) compared to White Europeans (WE) have more fat mass and are more insulin-resistant at a given body mass index (BMI). Whether these are reflected in other gestational metabolomic differences is unclear. Our aim was to compare gestational metabolic profiles and their determinants between WE and SA women. We used data from a United Kingdom (UK) cohort to compare metabolic profiles and associations of maternal age, education, parity, height, BMI, tricep skinfold thickness, gestational diabetes (GD), pre-eclampsia, and gestational hypertension with 156 metabolic measurements in WE (n = 4072) and SA (n = 4702) women. Metabolic profiles, measured in fasting serum taken between 26–28 weeks gestation, were quantified by nuclear magnetic resonance. Distributions of most metabolic measures differed by ethnicity. WE women had higher levels of most lipoprotein subclasses, cholesterol, glycerides and phospholipids, monosaturated fatty acids, and creatinine but lower levels of glucose, linoleic acid, omega-6 and polyunsaturated fatty acids, and most amino acids. Higher BMI and having GD were associated with higher levels of several lipoprotein subclasses, triglycerides, and other metabolites, mostly with stronger associations in WEs. We have shown differences in gestational metabolic profiles between WE and SA women and demonstrated that associations of exposures with these metabolites differ by ethnicity.

Examining the predictive accuracy of metabolomics for small-for-gestational-age babies: a systematic review

INTRODUCTION

To date, there is no robust enough test to predict small-for-gestational-age (SGA) infants, who are at increased lifelong risk of morbidity and mortality.

OBJECTIVE

To determine the accuracy of metabolomics in predicting SGA babies and elucidate which metabolites are predictive of this condition.

DATA SOURCES

Two independent researchers explored 11 electronic databases and grey literature in February 2018 and November 2018, covering publications from 1998 to 2018. Both researchers performed data extraction and quality assessment independently. A third researcher resolved discrepancies.

STUDY ELIGIBILITY CRITERIA

Cohort or nested case-control studies were included which investigated pregnant women and performed metabolomics analysis to evaluate SGA infants. The primary outcome was birth weight <10th centile-as a surrogate for fetal growth restriction-by population-based or customised charts.

STUDY APPRAISAL AND SYNTHESIS METHODS

Two independent researchers extracted data on study design, obstetric variables and sampling, metabolomics technique, chemical class of metabolites, and prediction accuracy measures. Authors were contacted to provide additional data when necessary.

RESULTS

A total of 9181 references were retrieved. Of these, 273 were duplicate, 8760 were removed by title or abstract, and 133 were excluded by full-text content. Thus, 15 studies were included. Only two studies used the fifth centile as a cut-off, and most reports sampled second-trimester pregnant women. Liquid chromatography coupled to mass spectrometry was the most common metabolomics approach. Untargeted studies in the second trimester provided the largest number of predictive metabolites, using maternal blood or hair. Fatty acids, phosphosphingolipids and amino acids were the most prevalent predictive chemical subclasses.

CONCLUSIONS AND IMPLICATIONS

Significant heterogeneity of participant characteristics and methods employed among studies precluded a meta-analysis. Compounds related to lipid metabolism should be validated up to the second trimester in different settings.

PROSPERO REGISTRATION NUMBER

CRD42018089985.

A double-hit pre-eclampsia model results in sex-specific growth restriction patterns

Pre-eclampsia is a multifactorial pregnancy-associated disorder characterized by angiogenic dysbalance and systemic inflammation; however, animal models that combine these two pathophysiological conditions are missing. Here, we introduce a novel double-hit pre-eclampsia mouse model that mimics the complex multifactorial conditions present during pre-eclampsia and allows for the investigation of early consequences for the fetus. Adenoviral overexpression of soluble fms-like tyrosine kinase (sFlt-1) and lipopolysaccharide (LPS) administration at mid-gestation in pregnant mice resulted in hypertension and albuminuria comparable to that of the manifestation in humans. A metabolomics analysis revealed that pre-eclamptic dams have increased plasma concentrations of phosphadytilcholines. The fetuses of both sexes were growth restricted; however, in males a brain-sparing effect was seen as compensation for this growth restriction. According to the plasma metabolomics, male fetuses showed changes in amino acid metabolism, while female fetuses showed pronounced alterations in lipid metabolism. Our results show that combined exposure to sFlt-1 and LPS mimics the clinical symptoms of pre-eclampsia and affects fetal growth in a sex-specific manner, with accompanying metabolome changes.

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.

Preliminary metabolomics analysis of placenta in maternal obesity

INTRODUCTION

Metabolomics identifies phenotypical groups with specific metabolic profiles, being increasingly applied to several pregnancy conditions. This is the first preliminary study analyzing placental metabolomics in normal weight (NW) and obese (OB) pregnancies.

METHODS

Twenty NW (18.5 ≤ BMI< 25 kg/m²) and eighteen OB (BMI≥ 30 kg/m²) pregnancies were studied. Placental biopsies were collected at elective caesarean section. Metabolites extraction method was optimized for hydrophilic and lipophilic phases, then analyzed with GC-MS. Univariate and PLS-DA multivariate analysis were applied.

RESULTS

Univariate analysis showed increased uracil levels while multivariate PLS-DA analysis revealed lower levels of LC-PUFA derivatives in the lipophilic phase and several metabolites with significantly different levels in the hydrophilic phase of OB vs NW.

DISCUSSION

Placental metabolome analysis of obese pregnancies showed differences in metabolites involved in antioxidant defenses, nucleotide production, as well as lipid synthesis and energy production, supporting a shift towards higher placental metabolism. OB placentas also showed a specific fatty acids profile suggesting a disruption of LC-PUFA biomagnification. This study can lay the foundation to further metabolomic placental characterization in maternal obesity. Metabolic signatures in obese placentas may reflect changes occurring in the intrauterine metabolic environment, which may affect the development of adult diseases.