Application of metabolomics to preeclampsia diagnosis

Diagnosis of preeclampsia using metabolomic biomarkers

The diagnostic criteria for preeclampsia do not accurately reflect the pathophysiological characteristics of patients with preeclampsia. Conventional biomarkers and diagnostic approaches have proven insufficient to fully comprehend the intricacies of preeclampsia. This study aimed to screen differentially abundant metabolites as candidate biomarkers for preeclampsia. A propensity score matching method was used to perform a 1:1 match between preeclampsia patients (n = 70) and healthy control individuals (n = 70). Based on univariate and multivariate statistical analysis methods, the different characteristic metabolites were screened and identified. Least absolute shrinkage and selection operator (LASSO) regression analysis was subsequently used to further screen for differentially abundant metabolites. A receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic efficacy of the metabolites. A total of 1,630 metabolites were identified and quantified in maternal serum samples. Fifty-three metabolites were significantly increased, and two were significantly decreased in preeclampsia patients. The area under the curve (AUC) of the model composed of isobutyryl-L-carnitine and acetyl-leucine was 0.878, and the sensitivity and specificity in detecting preeclampsia were 81.4% and 87.1%, respectively. There are significant differences in metabolism between preeclampsia patients and healthy pregnant women, and a range of novel biomarkers have been identified. These findings lay the foundation for the use of metabolomic biomarkers for the diagnosis of preeclampsia.

Plasma metabolites are altered before and after diagnosis of preeclampsia or fetal growth restriction

Metabolomics is the study of small molecules (metabolites), within cells, tissues and biofluids. Maternal metabolites can provide important insight into the health and development of both mother and fetus throughout pregnancy. This study assessed metabolic profiles in the maternal circulation prior to and at the time of diagnosis of preeclampsia and fetal growth restriction. Maternal plasma samples were collected from two independent cohorts: (1) Established disease cohort: 50 participants diagnosed with early-onset preeclampsia (< 34 weeks' gestation), 14 with early-onset fetal growth restriction, and 25 gestation-matched controls. (2) Prospective cohort, collected at 36 weeks' gestation before diagnosis: 17 participants later developed preeclampsia, 49 delivered infants with fetal growth restriction (birthweight < 5th centile), and 72 randomly selected controls. Metabolic evaluation was performed by Metabolomics Australia on the Agilent 6545 QTOF Mass Spectrometer. In the established disease cohort, 77 metabolites were altered in circulation from participants with preeclampsia - increased L-cysteine (3.73-fold), L-cystine (3.28-fold), L-acetylcarnitine (2.57-fold), and carnitine (1.53-fold) (p < 0.05). There were 53 metabolites dysregulated in participants who delivered a fetal growth restriction infant-including increased levulinic acid, citric acid (1.93-fold), and creatine (1.14-fold) (p < 0.05). In the prospective cohort, 30 metabolites were altered in participants who later developed preeclampsia at term - reduced glutaric acid (0.85-fold), porphobilinogen (0.77-fold) and amininohippuric acid (0.82-fold) (p < 0.05) was observed. There were 5 metabolites altered in participants who later delivered a fetal growth restriction infant - including reduced 3-methoxybenzenepropanoic acid (p < 0.05). Downstream pathway analysis revealed aminoacyl-tRNA biosynthesis to be most significantly altered in the established cohort in preeclampsia (13/48 hits, p < 0.001) and fetal growth restriction (7/48 hits, p < 0.001). The predictive cohort showed no significant pathway alterations. This study observed altered metabolites in maternal plasma collected before and after diagnosis of a preeclampsia or fetal growth restriction. While a significant number of metabolites were altered with established disease, few changes were observed in the predictive cohort. Thus, metabolites measured in this study may not be useful as predictors of preeclampsia or fetal growth restriction.

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.

Investigating genetic links between blood metabolites and preeclampsia

BACKGROUND

Observational studies have revealed that metabolic disorders are closely related to the development of preeclampsia (PE). However, there is still a research gap on the causal role of metabolites in promoting or preventing PE. We aimed to systematically explore the causal association between circulating metabolites and PE.

METHODS

Single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) of 486 blood metabolites (7,824 participants) were extracted as instrumental variables (P < 1 × 10- 5), GWAS summary statistics for PE were obtained from FinnGen consortium (7,212 cases and 194,266 controls) as outcome, and a two-sample Mendelian randomization (MR) analysis was conducted. Inverse variance weighted (IVW) was set as the primary method, with MR-Egger and weighted median as auxiliary methods; the instrumental variable strength and confounding factors were also assessed. Sensitivity analyses including MR-Egger, Cochran's Q test, MR-PRESSO and leave-one-out analysis were performed to test the robustness of the MR results. For significant associations, repeated MR and meta-analysis were performed by another metabolite GWAS (8,299 participants). Furthermore, significantly associated metabolites were subjected to a metabolic pathway analysis.

RESULTS

The instrumental variables for the metabolites ranged from 3 to 493. Primary analysis revealed a total of 12 known (e.g., phenol sulfate, citrulline, lactate and gamma-glutamylglutamine) and 11 unknown metabolites were associated with PE. Heterogeneity and pleiotropy tests verified the robustness of the MR results. Validation with another metabolite GWAS dataset revealed consistency trends in 6 of the known metabolites with preliminary analysis, particularly the finding that genetic susceptibility to low levels of arachidonate (20:4n6) and citrulline were risk factors for PE. The pathway analysis revealed glycolysis/gluconeogenesis and arginine biosynthesis involved in the pathogenesis of PE.

CONCLUSIONS

This study identifies a causal relationship between some circulating metabolites and PE. Our study presented new perspectives on the pathogenesis of PE by integrating metabolomics with genomics, which opens up avenues for more accurate understanding and management of the disease, providing new potential candidate metabolic molecular markers for the prevention, diagnosis and treatment of PE. Considering the limitations of MR studies, further research is needed to confirm the causality and underlying mechanisms of these findings.

10. Role of high dimensional technology in preeclampsia (omics in preeclampsia)

Preeclampsia is a pregnancy-specific disease that has no known precise cause. Integrative biology approach based on multi-omics has been applied to identify upstream pathways and better understand the pathophysiology of preeclampsia. At DNA level, genomics and epigenomics studies have revealed numerous genetic variants associated with preeclampsia, including those involved in regulating blood pressure and immune response. Transcriptomics analyses have revealed altered expression of genes in preeclampsia, particularly those related to inflammation and angiogenesis. At protein level, proteomics studies have identified potential biomarkers for preeclampsia diagnosis and prediction in addition to revealing the main pathophysiological pathways involved in this disease. At metabolite level, metabolomics has highlighted altered lipid and amino acid metabolisms in preeclampsia. Finally, microbiomics studies have identified dysbiosis in the gut and vaginal microbiota in pregnant women with preeclampsia. Overall, omics technologies have improved our understanding of the complex molecular mechanisms underlying preeclampsia. However, further research is warranted to fully integrate and translate these omics findings into clinical practice.

Comparison of the metabolite profile of donkey and horse seminal plasma and its relationship with sperm viability and motility

Previous research revealed that several seminal plasma (SP) metabolites are related to sperm functionality, fertility, and preservation. While it is understood that variations between species exist, whether the SP metabolome differs between donkeys and horses has not been previously investigated. The aim of this work, therefore, was to characterize and compare donkey and horse SP metabolites using nuclear magnetic resonance (NMR) spectroscopy, and relate them to sperm viability and motility. For this purpose, ejaculates from 18 different donkeys and 18 different horses were collected and separated into two aliquots: one for harvesting the SP by centrifugation and obtaining the metabolic profile through NMR, and the other for evaluating sperm viability and motility. Based on total motility and sperm viability, samples were classified as with good (GQ) or poor (PQ) quality. The metabolomic profile of donkey and horse SP revealed the presence of 28 metabolites, which coincided in the two species. Yet, differences between horses and donkeys were observed in the concentration of 18 of these 28 metabolites, as well as between ejaculates classified as GQ or PQ and in the relationship of metabolites with sperm motility and viability. These findings suggest that sperm from donkeys and horses differ in their metabolism and energetic requirements, and that the concentration of specific SP metabolites may be related to sperm functionality. Further research should shed light on the metabolic needs of donkey and horse sperm, and evaluate how the knowledge collected from the contribution of these metabolites can help improve semen preservation in the two species.

Metabolomic insights into maternal and neonatal complications in pregnancies affected by type 1 diabetes

AIMS/HYPOTHESIS

Type 1 diabetes in pregnancy is associated with suboptimal pregnancy outcomes, attributed to maternal hyperglycaemia and offspring hyperinsulinism (quantifiable by cord blood C-peptide). We assessed metabolomic patterns associated with risk factors (maternal hyperglycaemia, diet, BMI, weight gain) and perinatal complications (pre-eclampsia, large for gestational age [LGA], neonatal hypoglycaemia, hyperinsulinism) in the Continuous Glucose Monitoring in Women with Type 1 Diabetes in Pregnancy Trial (CONCEPTT).

METHODS

A total of 174 CONCEPTT participants gave ≥1 non-fasting serum sample for the biorepository at 12 gestational weeks (147 women), 24 weeks (167 women) and 34 weeks (160 women) with cord blood from 93 infants. Results from untargeted metabolite analysis (ultrahigh performance LC-MS) are presented as adjusted logistic/linear regression of maternal and cord blood metabolites, risk factors and perinatal complications using a modified Bonferroni limit of significance for dependent variables.

RESULTS

Maternal continuous glucose monitoring time-above-range (but not BMI or excessive gestational weight gain) was associated with increased triacylglycerols in maternal blood and increased carnitines in cord blood. LGA, adiposity, neonatal hypoglycaemia and offspring hyperinsulinism showed distinct metabolite profiles. LGA was associated with increased carnitines, steroid hormones and lipid metabolites, predominantly in the third trimester. However, neonatal hypoglycaemia and offspring hyperinsulinism were both associated with metabolite changes from the first trimester, featuring triacylglycerols or dietary phenols. Pre-eclampsia was associated with increased abundance of phosphatidylethanolamines, a membrane phospholipid, at 24 weeks.

CONCLUSIONS/INTERPRETATION

Altered lipid metabolism is a key pathophysiological feature of type 1 diabetes pregnancy. New strategies for optimising maternal diet and insulin dosing from the first trimester are needed to improve pregnancy outcomes in type 1 diabetes.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Clinical and metabolomic characterization of Brivanib-Induced hypertension in metastatic colorectal cancer

BACKGROUND

Trials of tyrosine kinase inhibitors (TKI) have not demonstrated dramatic benefits in advanced colorectal cancer (CRC), and this may be a function of poor patient selection. TKI-induced hypertension is reportedly a surrogate marker for treatment benefit for some tumor types. Our objective was to determine whether hypertension was associated with benefit in the context of CRC treatment, and also to gain insight on the pathogenesis of TKI-induced hypertension by monitoring associated changes in the circulating metabolome.

PATIENTS AND METHODS

Clinical data were acquired from clinical trial patients with metastatic CRC randomized to cetuximab ± the TKI brivanib (N = 750). Outcomes were evaluated as a function of treatment-induced hypertension. For metabolomic studies, plasma samples were taken at baseline, as well as at 1, 4, and 12 weeks after treatment initiation. Samples were submitted to gas chromatography-mass spectrometry to identify treatment-related metabolomic changes associated with TKI-induced hypertension, compared to pre-treatment baseline. A model based on changes in metabolite concentrations was generated using orthogonal partial least squares discriminant analysis (OPLS-DA).

RESULTS

In the brivanib treated group, 95 patients had treatment-related hypertension within 12 weeks of initiating treatment. TKI-induced hypertension was not associated with a significantly higher response rate, nor was it associated with improved progression-free or overall survival. In metabolomic studies, 386 metabolites were identified. There were 29 metabolites that changed with treatment and distinguished patients with and without TKI-induced hypertension. The OPLS-DA model for brivanib-induced hypertension was significant and robust (R2 Y score = 0.89, Q2 Y score = 0.70, CV-ANOVA = 2.01 e-7). Notable metabolomic features previously reported in pre-eclampsia and associated with vasoconstriction were found.

CONCLUSION

TKI-induced hypertension was not associated with clinical benefit in metastatic CRC. We have identified changes in the metabolome that are associated with the development of worsening brivanib-induced hypertension that may be useful in future efforts of characterizing this toxicity.

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.

Detection of metabolic syndrome with ATR-FTIR spectroscopy and chemometrics in blood plasma

Metabolic Syndrome (MetS) is a constellation of 3 or more risk factor (abdominal obesity, high triglycerides, low HDL-c, high blood pressure, and elevated blood glucose) for atherosclerotic cardiovascular disease. Considering these systemic metabolic changes in the biochemical pathways of all biomolecules, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy is a rapid, low-cost, and reagent-free alternative technique capable of identifying spectral biomarkers that differentiate subjects with MetS from control. In this study, plasma samples from 74 subjects (14 MetS, 60 control) were analyzed on the ATR-FTIR spectrophotometer. The objective was to differentiate subjects with MetS from control with supervised chemometrics modeling (Orthogonal Partial Least Squares-Discriminant Analysis, OPLS-DA). Additionally, the inflammatory status of subjects with MetS and control (supervised by C-reactive protein - CRP, leptin, and cell-free DNA - cfDNA) was verified. The OPLS-DA model achieved 100% sensitivity and specificity in cross-validation. For 1 latent variable (93.4% of variance), RMSECV < 0.002, PRESS CV < 0.0001, and R² > 0.9999 was obtained. Significant spectrochemical differences (p < 0.05) were found between MetS and control subjects in the following biomolecular regions (cm^-1): 1717-1703 [ν(CO) and δ(NH)], 1166-1137 [ν(C-OH) + ν(CO) and ν(CC) + δ(OH) + ν(CO)], 1113-1040 [ν(PO₂^-) and ν(C-OH)], and 1027-1008 [ν(CO) and v(CH₂OH)]. In the OPLS-DA model loadings, amide I [1720-1600 cm^-1, ν(CO)] and amide II [1570-1480 cm^-1, δ(NH) + ν(CH)] had significantly greater weight than all other regions. There was a significant difference in inflammatory status between MetS patient and control (p < 0.05 for CRP and leptin, and p < 0.01 for cfDNA).

The Effects of Pregnancy on Amino Acid Levels and Nitrogen Disposition

Limited data are available on the effects of pregnancy on the maternal metabolome. Therefore, the objective of this study was to use metabolomics analysis to determine pathways impacted by pregnancy followed by targeted confirmatory analysis to provide more powerful conclusions about metabolic alterations during pregnancy. Forty-seven pregnant women, 18-50 years of age were included in this study, with each subject serving as their own control. Plasma samples were collected between 25 and 28 weeks gestation and again ≥3 months postpartum for metabolomics analysis utilizing an HILIC/UHPLC/MS/MS assay with confirmatory targeted specific concentration analysis for 10 of the significantly altered amino acids utilizing an LC/MS assay. Principle component analysis (PCA) on metabolomics data clearly separated pregnant and postpartum groups and identified outliers in a preliminary assessment. Of the 980 metabolites recorded, 706 were determined to be significantly different between pregnancy and postpartum. Pathway analysis revealed three significantly impacted pathways, arginine biosynthesis (p = 2 × 10-5 and FDR = 1 × 10-3), valine, leucine, and isoleucine metabolism (p = 2 × 10-5 and FDR = 2 × 10-3), and xanthine metabolism (p = 4 × 10-5 and FDR = 4 × 10-3). Of these we focused analysis on arginine biosynthesis and branched-chain amino acid (BCAA) metabolism due to their clinical importance and interconnected roles in amino acid metabolism. In the confirmational analysis, 7 of 10 metabolites were confirmed as significant and all 10 confirmed the direction of change of concentrations observed in the metabolomics analysis. The data support an alteration in urea nitrogen disposition and amino acid metabolism during pregnancy. These changes could also impact endogenous nitric oxide production and contribute to diseases of pregnancy. This study provides evidence for changes in both the ammonia-urea nitrogen and the BCAA metabolism taking place during pregnancy.

A comprehensive analysis of metabolomics and transcriptomics to reveal major metabolic pathways and potential biomarkers of human preeclampsia placenta

Background: The etiology of preeclampsia (PE) remains unclear. With the utilization of metabolomics, dysregulated production of several metabolic components in human plasma, such as lipids, amino acids, androgens and estrogens, was found to be important in the pathogenesis of PE. Transcriptomics adds more in-depth information, and the integration of transcriptomics and metabolomics may yield further insight into PE pathogenesis than either one alone.

Objectives: We investigated the placental metabolomics and transcriptomics of PE patients to identify affected metabolic pathways and potential biological targets for exploring the disease pathogenesis.

Methods: Integrated transcriptomics and metabolomics were used to analyze five paired human placentas from patients with severe PE and normal pregnancies. This was followed by further validation of our findings in a publicly available dataset of 173 PE vs. 157 control placentas. In addition, weighted gene coexpression network construction was performed to assess the correlation between genetic alterations and diseases.

Results: We identified 66 and 41 differentially altered metabolites in negative and positive ion modes, respectively, in the PE group compared to the control group, and found 2,560 differentially expressed genes. Several pathways were aberrantly altered in the PE placenta at both the metabolic and transcriptional levels, including steroid hormone biosynthesis, the cAMP signaling pathway, neuroactive ligand–receptor interactions, taste transduction and prion diseases. Additionally, we found 11 differential metabolites and 11 differentially expressed genes involved in the steroid hormone biosynthesis pathway, indicating impaired metabolism of steroid hormones in the PE placenta. Furthermore, we found that CYP11A1, HSD3B2, and HSD17B6 are highly correlated with diseases.

Conclusion: Our findings provide a profile of the dysregulated steroid hormone biosynthesis in PE placenta, we observed a dysregulated cortisol-to-cortisone ratio, testosterone accumulation, decreased testosterone downstream metabolites, impaired production of estrone and estriol, and aberrant hydroxylation and methylation of estradiol. Disorders of placental steroid hormone metabolism might be a consequence or a compensatory change in pathological placentation in PE, which underscores the need to investigate the physiology of steroid hormone metabolites in the etiology of PE.

Robust identification of temporal biomarkers in longitudinal omics studies

MOTIVATION

Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including non-uniform sampling intervals, missing data points, subject dropout and differing numbers of samples per subject.

RESULTS

In this work, we developed OmicsLonDA, a statistical method that provides robust identification of time intervals of temporal omics biomarkers. OmicsLonDA is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked OmicsLonDA on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.87. Applying OmicsLonDA to the iPOP cohort revealed temporal patterns of genes, proteins, metabolites and microbes that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied OmicsLonDA to a longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and OmicsLonDA identified potential lipid markers that are temporally significantly different between the two groups.

AVAILABILITY AND IMPLEMENTATION

We provide an open-source R package (https://bioconductor.org/packages/OmicsLonDA), to enable widespread use.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data

Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments. We developed a digital phenotyping algorithm to curate 108,557 pregnancies from EMRs across the Mount Sinai Health System, accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset (N = 60,879) to construct predictive models of preeclampsia across three major pregnancy time periods (ante-, intra-, and postpartum). The resulting models predicted preeclampsia with high accuracy across the different pregnancy periods, with areas under the receiver operating characteristic curves (AUC) of 0.92, 0.82, and 0.89 at 37 gestational weeks, intrapartum and postpartum, respectively. We observed comparable performance in two independent patient cohorts. While our machine learning approach identified known risk factors of preeclampsia (such as blood pressure, weight, and maternal age), it also identified other potential risk factors, such as complete blood count related characteristics for the antepartum period. Our model not only has utility for earlier identification of patients at risk for preeclampsia, but given the prediction accuracy exceeds what is currently achieved in clinical practice, our model provides a path for promoting personalized precision therapeutic strategies for patients at risk.

The Relationship between Angiogenic Factors and Energy Metabolism in Preeclampsia

Antiangiogenic factors are currently used for the prediction of preeclampsia. The present study aimed to evaluate the relationship between antiangiogenic factors and lipid and carbohydrate metabolism in maternal plasma and placenta. We analyzed 56 pregnant women, 30 healthy and 26 with preeclampsia (including early and late onset). We compared antiangiogenic factors soluble Fms-like Tyrosine Kinase-1 (sfLt-1), placental growth factor (PlGF), and soluble endoglin (sEng)), lipid and carbohydrate metabolism in maternal plasma, and lipid metabolism in the placenta from assays of fatty acid oxidation, fatty acid esterification, and triglyceride levels in all groups. Antiangiogenic factors sFlt-1, sFlt-1/PlGF ratio, and sEng showed a positive correlation with triglyceride, free fatty acid, and C-peptide maternal serum levels. However, there was no relationship between angiogenic factors and placental lipid metabolism parameters. Free fatty acids were predictive of elevated sFlt-1 and sEng, while C-peptide was predictive of an elevated sFlt1/PlGF ratio. The findings in this study generate a model to predict elevated antiangiogenic factor values and the relationship between them with different products of lipid and carbohydrate metabolism in maternal serum and placenta in preeclampsia.

Prediction of pregnancy-related hypertensive disorders using metabolomics: a systematic review

OBJECTIVE

To determine the accuracy of metabolomics in predicting hypertensive disorders in pregnancy.

DESIGN

Systematic review of observational studies.

DATA SOURCES AND STUDY ELIGIBILITY CRITERIA

An electronic literature search was performed in June 2019 and February 2022. Two researchers independently selected studies published between 1998 and 2022 on metabolomic techniques applied to predict the condition; subsequently, they extracted data and performed quality assessment. Discrepancies were dealt with a third reviewer. The primary outcome was pre-eclampsia. Cohort or case-control studies were eligible when maternal samples were taken before diagnosis of the hypertensive disorder.

STUDY APPRAISAL AND SYNTHESIS METHODS

Data on study design, maternal characteristics, how hypertension was diagnosed, metabolomics details and metabolites, and accuracy were independently extracted by two authors.

RESULTS

Among 4613 initially identified studies on metabolomics, 68 were read in full text and 32 articles were included. Studies were excluded due to duplicated data, study design or lack of identification of metabolites. Metabolomics was applied mainly in the second trimester; the most common technique was liquid-chromatography coupled to mass spectrometry. Among the 122 different metabolites found, there were 23 amino acids and 21 fatty acids. Most of the metabolites were involved with ammonia recycling; amino acid metabolism; arachidonic acid metabolism; lipid transport, metabolism and peroxidation; fatty acid metabolism; cell signalling; galactose metabolism; nucleotide sugars metabolism; lactose degradation; and glycerolipid metabolism. Only citrate was a common metabolite for prediction of early-onset and late-onset pre-eclampsia. Vitamin D was the only metabolite in common for pre-eclampsia and gestational hypertension prediction. Meta-analysis was not performed due to lack of appropriate standardised data.

CONCLUSIONS AND IMPLICATIONS

Metabolite signatures may contribute to further insights into the pathogenesis of pre-eclampsia and support screening tests. Nevertheless, it is mandatory to validate such methods in larger studies with a heterogeneous population to ascertain the potential for their use in clinical practice.

PROSPERO REGISTRATION NUMBER

CRD42018097409.

Metabolomic Markers for Predicting Preeclampsia in the First Trimester of Pregnancy: A Retrospective Study

We sought to identify the characteristic metabolite profile of blood plasma samples obtained from patients with preeclampsia. Direct high-resolution mass spectrometry was used to analyze samples from 79 pregnant women, 34 of whom had preeclampsia. We performed a comparative analysis of the metabolite profiles and found that they differed between pregnant women with and without preeclampsia. Lipids and sugars were identified as components of the metabolite profile that are likely to be associated with the development of preeclampsia. While PE was established only in the third trimester, a set of metabolites specific for the third trimester, including 2-(acetylamino)-1,5-anhydro-2-deoxy-4-O-b-D-galactopyranosyl-D-arabino-Hex-1-enitol, N-Acetyl-D-glucosaminyldiphosphodolichol, Cer(d18:0/20:0), and allolithocholic acid, was already traced in the first trimester. These components are also likely involved in lipid metabolism disorders and the development of oxidative stress.

Proteomics and Metabolomics Profiling of Platelets and Plasma Mediators of Thrombo-Inflammation in Gestational Hypertension and Preeclampsia

Platelets may be pivotal mediators of the thrombotic and coagulopathic complications of preeclampsia (PE), linking inflammation and thrombosis with endothelial and vascular dysfunction. Both PE and gestational hypertension (GH) fall within the spectrum of hypertensive complications of pregnancy, with GH being a risk factor for preeclampsia. However, it is unclear what biomarkers distinguish PE from GH. Using a discovery size cohort, we aimed to characterize specific plasma and platelet thrombo-inflammatory drivers indicative of PE and differentiate PE from GH. We performed multiplex immunoassays, platelet and plasma quantitative proteomics and metabolomics of PE patients, comparing with non-pregnant (NP), healthy pregnant controls (PC) and GH participants. The expression pattern of plasma proteins and metabolites in PE/GH platelets was distinct from that of NP and PC. Whilst procoagulation in PC may be fibrinogen driven, inter-alpha-trypsin inhibitors ITIH2 and ITIH3 are likely mediators of thrombo-inflammation in GH and PE, and fibronectin and S100A8/9 may be major procoagulant agonists in PE only. Also enriched in PE were CCL1 and CCL27 plasma cytokines, and the platelet leucine-rich repeat-containing protein 27 and 42 (LRRC27/42), whose effects on platelets were explored using STRING analysis. Through protein-protein interactions analysis, we generated a new hypothesis for platelets’ contribution to the thrombo-inflammatory states of preeclampsia.

Identification of Biomarkers for Preeclampsia Based on Metabolomics

Background

Preeclampsia (PE) is a significant cause of maternal and neonatal morbidity and mortality worldwide. However, the pathogenesis of PE is unclear and reliable early diagnostic methods are still lacking. The purpose of this review is to summarize potential metabolic biomarkers and pathways of PE, which might facilitate risk prediction and clinical diagnosis, and obtain a better understanding of specific metabolic mechanisms of PE.

Methods

This review included human metabolomics studies related to PE in the PubMed, Google Scholar, and Web of Science databases from January 2000 to November 2021. The reported metabolic biomarkers were systematically examined and compared. Pathway analysis was conducted through the online software MetaboAnalyst 5.0.

Results

Forty-one human studies were included in this systematic review. Several metabolites, such as creatinine, glycine, L-isoleucine, and glucose and biomarkers with consistent trends (decanoylcarnitine, 3-hydroxyisovaleric acid, and octenoylcarnitine), were frequently reported. In addition, eight amino acid metabolism-related, three carbohydrate metabolism-related, one translation-related and one lipid metabolism-related pathways were identified. These biomarkers and pathways, closely related to renal dysfunction, insulin resistance, lipid metabolism disorder, activated inflammation, and impaired nitric oxide production, were very likely to contribute to the progression of PE.

Conclusion

This study summarized several metabolites and metabolic pathways, which may be associated with PE. These high-frequency differential metabolites are promising to be biomarkers of PE for early diagnosis, and the prominent metabolic pathway may provide new insights for the understanding of the pathogenesis of PE.

NMR-Based Metabolomics Analysis Predicts Response to Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most fatal type of breast cancer (BC). Due to the lack of relevant targeted drug therapy, in addition to surgery, chemotherapy is still the most common treatment option for TNBC. TNBC is heterogeneous, and different patients have an unusual sensitivity to chemotherapy. Only part of the patients will benefit from chemotherapy, so neoadjuvant chemotherapy (NAC) is controversial in the treatment of TNBC. Here, we performed an NMR spectroscopy–based metabolomics study to analyze the relationship between the patients’ metabolic phenotypes and chemotherapy sensitivity in the serum samples. Metabolic phenotypes from patients with pathological partial response, pathological complete response, and pathological stable disease (pPR, pCR, and pSD) could be distinguished. Furthermore, we conducted metabolic pathway analysis based on identified significant metabolites and revealed significantly disturbed metabolic pathways closely associated with three groups of TNBC patients. We evaluated the discriminative ability of metabolites related to significantly disturbed metabolic pathways by using the multi-receiver–operating characteristic (ROC) curve analysis. Three significantly disturbed metabolic pathways of glycine, serine, and threonine metabolism, valine, leucine, and isoleucine biosynthesis, and alanine, aspartate, and glutamate metabolism could be used as potential predictive models to distinguish three types of TNBC patients. These results indicate that a metabolic phenotype could be used to predict whether a patient is suitable for NAC. Metabolomics research could provide data in support of metabolic phenotypes for personalized treatment of TNBC.

Monitoring motherhood: Monitoring and optimizing glycaemia in women with pre-existing diabetes in pregnancy

Despite recent advances in care, women with diabetes in pregnancy are still at increased risk of multiple pregnancy complications. Offspring exposed to hyperglycaemia in utero also experience long-term health sequelae, affecting neurocognitive and cardiometabolic status. Many of these adverse consequences can be prevented or ameliorated with good medical care, specifically to optimize glycaemic control. The accurate assessment of glycaemia in pregnancy is therefore vital to safeguard the health of mother and child. However, there is no consensus about the best method of monitoring glycaemic control in pregnancy. Short-term changes in insulin dosage and lifestyle, with altered appetite, insulin sensitivity and red cell turnover create difficulties in interpretation of standard laboratory measures such as HbA_1c. The ideal marker would provide short-term feedback on daily or weekly glycaemic control, with additional capability to predict pregnancies at high risk of suboptimal outcomes. Several novel biochemical markers are available which allow assessment of dynamic changes in glycaemia over weeks rather than months. Continuous glucose monitoring devices have advanced in accuracy and provide new opportunities for robust assessment of glycaemia in pregnancy. Recent work from the continuous glucose monitoring in pregnant women with type 1 diabetes trial (CONCEPTT) has provided information about the ability of different markers of glycaemia to predict pregnancy outcomes.The aim of this review is to summarize the care for women with pre-existing diabetes in pregnancy and to highlight the important role of glycaemic monitoring in pregnancy.

Clinical significance of microRNA-320a and insulin-like growth factor-1 receptor in early-onset preeclampsia patients

AIMS

Currently, there is no reliable method to effectively predict and diagnose early-onset preeclampsia (EOPE). microRNAs (miRs) are promising biomarkers for EOPE. This study investigated the role of miR-320a in EOPE.

METHODS

Expressions of miR-320a and insulin-like growth factor-1 receptor (IGF-1R) in serum of EOPE patients and normal pregnant women were detected. The clinical diagnostic efficacy of miR-320a and IGF-1R for EOPE was analyzed using receiver operating characteristic curve. The correlation between miR-320a expression and EOPE clinical indicators [mean arterial pressure (MAP), 24-h urinary protein excretion, serum creatinine (SCR), uric acid (UA), albumin (ALB) and platelet count] was analyzed. The correlation and binding relationship between miR-320a and IGF-1R was predicted and verified.

RESULTS

miR-320a was upregulated, and IGF-1R was downregulated in EOPE patients with their differential expressions more obvious in severe EOPE than mild EOPE. miR-320a and IGF-1R possessed potent clinical diagnostic efficacy for EOPE. miR-320a expression showed a positive correlation with MAP, 24-h urinary protein excretion, UA and SCR levels, and a negative correlation with ALB level and platelet count in EOPE patients. Moreover, miR-320a targeted IGF-1R.

CONCLUSION

We demonstrated that miR-320a was aberrantly elevated in EOPE and showed powerful clinical diagnostic efficacy for EOPE, which may be achieved by directly targeting IGF-1R. This study provided great reference values for EOPE early diagnosis and novel targets for EOPE treatment.

Metabolite Profiling of Pig Seminal Plasma Identifies Potential Biomarkers for Sperm Resilience to Liquid Preservation

Metabolomic approaches allow the study of downstream gene expression events since metabolites are considered as the products of cell signaling pathways. For this reason, many studies in humans have already been conducted to determine the influence of the metabolites present in seminal plasma (SP) on sperm physiology, and to identify putative biomarkers. However, in livestock species, these relationships are yet to be uncovered. Thus, the present study aimed to explore: (i) if concentrations of metabolites in pig SP are related to sperm quality and functionality, and (ii) if they could predict the sperm resilience to liquid storage at 17°C. To this end, 28 ejaculates were individually collected and split into three aliquots: one was used for SP analysis through nuclear magnetic resonance (NMR) spectroscopy; another served for the evaluation of sperm concentration and morphology; and the last one was utilized to determine sperm functionality parameters using computer-assisted sperm analysis (CASA) and flow cytometry after 0 h and 72 h of liquid-storage at 17°C. NMR analysis allowed the identification and quantification of 23 metabolites present in pig SP which, except for fumarate, were not observed to follow a breed-dependent behavior. Moreover, specific relationships between metabolites and sperm variables were identified: (i) glutamate, methanol, trimethylamine N-oxide, carnitine, and isoleucine were seen to be related to some sperm quality and functionality parameters evaluated immediately after semen collection; (ii) leucine, hypotaurine, carnitine and isoleucine were found to be associated to the sperm ability to withstand liquid storage; and (iii) Bayesian multiple regression models allowed the identification of metabolite patterns for specific sperm parameters at both 0 h and 72 h. The identification of these relationships opens up the possibility of further investigating these metabolites as potential sperm functional biomarkers.

Serum complement proteomics reveal biomarkers for hypertension disorder of pregnancy and the potential role of Clusterin

INTRODUCTION

Hypertension disorder of pregnancy (HDP) is one of the leading causes of maternal and foetal illness. The aim of the current study was to identify and verify novel serum markers for HDP.

METHODS

A label-free LC-MS/MS method was used to establish the serum proteomic profiles of 12 pre-HDP (before clinical diagnosis of HDP) pregnancies and verify prioritized candidates in the verification set of 48 pre-HDP pregnancies. These biomarkers were revalidated by ELISA in an independent cohort of 88 pre-HDP pregnancies. Subsequently, the candidate biomarkers were histologically analysed by immunohistochemistry, and function was evaluated in TEV-1 cells.

RESULTS

We identified 33 proteins with significantly increased abundance and 14 with decreased abundance (peptide FDR ≤ 1%, P < 0.05). Complement was one of the top enriched components in the pre-HDP group compared with the control group. Three complement factors (CLU, CFHR5, and CRP) were significantly increased in the three sets, of which CLU was a critical factor for the development of HDP (OR = 1.22, P < 0.001). When these three factors and body weight were combined, the AUC was 0.74, with a sensitivity of 0.67 and specificity of 0.68 for HDP prediction compared with normal pregnancy. In addition, inflammation-induced CLU could inhibit the invasion of TEV-1 cells.

CONCLUSIONS

Complement proteins may play an essential role in the occurrence of HDP by acting on trophoblast cells. CLU may be a high-risk factor for HDP, and the models combining candidates show reasonable screening efficiency of HDP in the first half of pregnancy.

Application of the Electronic Nose in Predicting Preeclampsia in High-risk Pregnancies. Pilot Study

BACKGROUND

Preeclampsia is a syndrome that affects 2-8 % of pregnancies worldwide and is the leading cause of maternal death. Therefore, early detection is crucial to identify women who require clinical monitoring during pregnancy and to evaluate new preventive therapies before clinical symptoms occur.

METHODS

The chemical fingerprints of the urine from three study groups pregnant with Preeclampsia, Healthy Pregnant (HP) and pregnant at High Risk of Preeclampsia (HRP) were evaluated using an electronic nose and the data obtained were subjected to principal component analysis (PCA), Canonical Analysis of Principal Coordinates (CAP), Partial Least Squares - Discriminant Analysis (PLS-DA) and ROC curves to determine the diagnostic power of the test.

RESULTS

A separation was found between the patients with preeclampsia and HP explaining 99% of the variability of the data. Subsequently, a CAP was obtained with a correct classification of 100%, and the PLS-DA was obtained an accuracy of 88%. With the results of axis CAP1, a ROC curve was performed resulting in a sensitivity of 100% and a specificity of 95.5%. Based on the CAP model it was found that 36% (n=9) of the HRP patients would develop preeclampsia based on the metabolites found in urine.

CONCLUSION

metabolomics can be used as a tool for early detection of preeclampsia in high-risk pregnant women, using portable olfactory technology.

Expression of Estrogen Receptor α by Decidual Macrophages in Preeclampsia

Preeclampsia is a gestation-associated hypertensive syndrome that threatens the life and health of the mother and the child. The condition is presumably caused by systemic failure with a strong involvement of innate immunity. In particular, it has been associated with flexible phenotypes of macrophages, which depend on the molecules circulating in the blood and tissue fluid, such as cytokines and hormones. This study aimed at a comparative evaluation of pro-inflammatory (TNFα) and anti-inflammatory (CD206, MMP9, HGF) markers, as well as the levels of estrogen receptor α, expressed by decidual macrophages in normal pregnancy and in patients with early- and late-onset preeclampsia. The tissue samples of decidua basalis were examined by immunohistochemistry and Western blotting. Isolation of decidual macrophages and their characterization were performed using cultural methods, flow cytometry and real-time PCR. Over 50% of the isolated decidual macrophages were positive for the pan-macrophage marker CD68. In the early-onset preeclampsia group, the levels of estrogen receptor α in decidua were significantly decreased. Furthermore, significantly decreased levels of HGF and CD206 were observed in both preeclampsia groups compared with the control group. The observed downregulation of estrogen receptor α, HGF and CD206 may contribute to the balance of pro- and anti-inflammatory macrophages and thereby to pathogenesis of preeclampsia.

Role of maternal nutrition and oxidative stress in placental telomere attrition in women with preeclampsia

Background:Maternal nutrition influences the growth and development of the fetus and influences pregnancy outcome. We have earlier demonstrated altered maternal nutrition and increased oxidative stress in women with preeclampsia. Oxidative stress is known to be associated with reduced telomere length and short telomere aggregates. Increased telomere attrition leads to increased cellular senescence and tissue ageing. Methods:The present review focuses on the role of maternal nutrition and oxidative stress in telomere attrition in preeclampsia. Results and Conclusion:Future studies need to examine the association between maternal nutritional status in early pregnancy, oxidative stress and telomere attrition in preeclampsia.

Nuclear magnetic resonance-based metabolomic analysis in the assessment of preclinical atherosclerosis in type 1 diabetes and preeclampsia

AIMS

Evaluate the role of plasma metabolomics in atherosclerosis according to the presence of type 1 diabetes (T1D) or previous preeclampsia.

METHODS

We recruited 105 women without cardiovascular disease and last pregnancy ≥5 years previously, divided according to the presence of T1D or previous preeclampsia. Preclinical atherosclerosis was defined as the presence of carotid plaque (intima-media thickness ≥1.5 mm) assessed by ultrasonography. Metabolomics were evaluated by nuclear magnetic resonance (NMR). Bivariate and multivariate-adjusted differences in NMR-metabolomics were evaluated.

RESULTS

The participants were 44.9 ± 8.1 years-old; 20% harbored plaques. There were significant differences in lipidic-, energetic- and nitrogen-related metabolites according to the presence of T1D/preeclampsia (p < 0.05). In multivariate-adjusted models (by age, statins, blood pressure and T1D/preeclampsia), only lipidomic-related metabolites were associated with atherosclerosis in the whole sample. However, stronger associations were observed in women with previous preeclampsia (vs. without; per 0.5 mmol/L increments); phosphatidylcholine, OR 4.08 (1.32-27.22); free cholesterol, 5.18 (1.22-21.97); saturated fatty acids, OR 2.99 (1.37-6.48); w-7, OR 2.29 (1.15-4.56); and w-9 fatty acids, OR 1.49 (1.00-2.23).

CONCLUSIONS

NMR-metabolomics showed a differential pattern according to the presence of T1D/preeclampsia in relation to preclinical atherosclerosis. Since most of these metabolites mirror lifestyle factors, they could help tailor dietetic advice in high-risk women.

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

BACKGROUND

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

OBJECTIVES

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

DESIGN

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

RESULTS

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

CONCLUSIONS

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

Metabolomic biomarkers in midtrimester maternal plasma can accurately predict the development of preeclampsia

Early identification of patients at risk of developing preeclampsia (PE) would allow providers to tailor their prenatal management and adopt preventive strategies, such as low-dose aspirin. Nevertheless, no mid-trimester biomarkers have as yet been proven useful for prediction of PE. This study investigates the ability of metabolomic biomarkers in mid-trimester maternal plasma to predict PE. A case–control study was conducted including 33 pregnant women with mid-trimester maternal plasma (gestational age [GA], 16–24 weeks) who subsequently developed PE and 66 GA-matched controls with normal outcomes (mid-trimester cohort). Plasma samples were comprehensively profiled for primary metabolic and lipidomic signatures based on gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and liquid chromatography Orbitrap mass spectrometry (LC-Orbitrap MS). A potential biomarker panel was computed based on binary logistic regression and evaluated using receiver operating characteristic (ROC) analysis. To evaluate whether this panel can be also used in late pregnancy, a retrospective cohort study was conducted using plasma collected from women who delivered in the late preterm period because of PE (n = 13) or other causes (n = 21) (at-delivery cohort). Metabolomic biomarkers were compared according to the indication for delivery. Performance of the metabolomic panel to identify patients with PE was compared also to a commonly used standard, the plasma soluble fms-like tyrosine kinase-1/placental growth factor (sFlt-1/PlGF) ratio. In the mid-trimester cohort, a total of 329 metabolites were identified and semi-quantified in maternal plasma using GC-TOF MS and LC-Orbitrap-MS. Binary logistic regression analysis proposed a mid-trimester biomarker panel for the prediction of PE with five metabolites (SM C28:1, SM C30:1, LysoPC C19:0, LysoPE C20:0, propane-1,3-diol). This metabolomic model predicted PE better than PlGF (AUC [95% CI]: 0.868 [0.844–0.891] vs 0.604 [0.485–0.723]) and sFlt-1/PlGF ratio. Analysis of plasma from the at-delivery cohort confirmed the ability of this biomarker panel to distinguish PE from non-PE, with comparable discrimination power to that of the sFlt-1/PlGF ratio. In conclusion, an integrative metabolomic biomarker panel in mid-trimester maternal plasma can accurately predict the development of PE and showed good discriminatory power in patients with PE at delivery.

Non-targeted urinary metabolomics in pregnancy and associations with fetal growth restriction

Our objective was to identify metabolites associated with fetal growth restriction (FGR) by examining early and late pregnancy differences in non-targeted urinary metabolites among FGR cases and non-FGR controls. An exploratory case-control study within LIFECODES birth cohort was performed. FGR cases (N = 30), defined as birthweight below the 10^th percentile, were matched with controls (N = 30) based on maternal age, race, pre-pregnancy body mass index, and gestational age at delivery. Gas chromatography/electron-ionization mass spectrometry was performed on urine samples collected at 10 and 26 weeks of gestation. Differences in urinary metabolite levels in cases and controls at each time point and between the two time points were calculated and then changes compared across pregnancy. 137 unique urinary metabolites were annotated, and several identified that were higher in cases compared to controls. For example, urinary concentrations of benzoic acid were higher in cases compared to controls at both study visits (3.01-fold higher in cases at visit 1, p < 0.01; 3.10-fold higher in cases at visit 3, p = 0.05). However, these findings from our exploratory analysis were not robust to false-discovery-rate adjustment. In conclusion, using a high-resolution, non-targeted approach, we found specific urinary organic acids differed over pregnancy by FGR case status.

A review of omics approaches to study preeclampsia

Preeclampsia is a medical condition affecting 5-10% of pregnancies. It has serious effects on the health of the pregnant mother and developing fetus. While possible causes of preeclampsia are speculated, there is no consensus on its etiology. The advancement of big data and high-throughput technologies enables to study preeclampsia at the new and systematic level. In this review, we first highlight the recent progress made in the field of preeclampsia research using various omics technology platforms, including epigenetics, genome-wide association studies (GWAS), transcriptomics, proteomics and metabolomics. Next, we integrate the results in individual omic level studies, and show that despite the lack of coherent biomarkers in all omics studies, inhibin is a potential preeclamptic biomarker supported by GWAS, transcriptomics and DNA methylation evidence. Using network analysis on the biomarkers of all the literature reviewed here, we identify four striking sub-networks with clear biological functions supported by previous molecular-biology and clinical observations. In summary, omics integration approach offers the promise to understand molecular mechanisms in preeclampsia.

Preeclampsia in 2018: Revisiting Concepts, Physiopathology, and Prediction

Preeclampsia currently remains one of the leading causes of death and severe maternal morbidity. Although its prevalence is still underestimated in some places due to underreporting, preeclampsia is a disease that health professionals need to know how to deal with and take action. For this reason, the studies about the theme remain along with the advances in their understanding that often implies improvement and change of concepts and conducts. The complexity of its etiology is a challenge and requires further studies for its full understanding. Apparently, poor adaptation of the maternal organism to the conceptus, marked by the nonoccurrence of changes in the uterine spiral arteries, determines a series of systemic repercussions that compound the various forms of preeclampsia presentation. In recent years, the use of acetylsalicylic acid to prevent cases of early onset of the disease has been consolidated and, alongside, studies have advanced the development of accessible and effective methods of identifying women at risk of preeclampsia. The aim of this review is to discuss updates on the occurrence, concept, pathophysiology, repercussion, prevention, and prediction of preeclampsia.