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

Development of a spontaneous preterm birth predictive model using a panel of serum protein biomarkers for early pregnant women: A nested case-control study

OBJECTIVE

To develop a model based on maternal serum liquid chromatography tandem mass spectrometry (LC-MS/MS) proteins to predict spontaneous preterm birth (sPTB).

METHODS

This nested case-control study used the data from a cohort of 2053 women in China from July 1, 2018, to January 31, 2019. In total, 110 singleton pregnancies at 11-13+6 weeks of pregnancy were used for model development and internal validation. A total of 72 pregnancies at 20-32 weeks from an additional cohort of 2167 women were used to evaluate the scalability of the model. Maternal serum samples were analyzed by LC-MS/MS, and a predictive model was developed using machine learning algorithms.

RESULTS

A novel predictive panel with four proteins, including soluble fms-like tyrosine kinase-1, matrix metalloproteinase 8, ceruloplasmin, and sex-hormone-binding globulin, was developed. The optimal model of logistic regression had an AUC of 0.934, with additional prediction of sPTB in second and third trimester (AUC = 0.868).

CONCLUSION

First-trimester modeling based on maternal serum LC-MS/MS identifies pregnant women at risk of sPTB, which may provide utility in identifying women at risk at an early stage of pregnancy before clinical presentation to allow for earlier intervention.

Tracing the Lipid Fingerprints of Preeclampsia

Preeclampsia (PE) is the most common pregnancy-related complication responsible for maternal mortality and morbidity. PE pathogenesis is characterized by placental dysfunction, impaired invasion of trophoblast, and defective spiral artery remodelling. Even after many years of research on PE, the etiology and pathophysiology of PE is still elusive. Our earlier studies have shown deregulated maternal and placental fatty acid and lipid metabolism to be associated with the pathogenesis of PE. Currently available lipidomics data have shown that glycerophospholipids, sphingolipid and cholesterol metabolism are mainly altered in preeclampsia. Including these five metabolites (SM C28:1, SM C30:1, LPC C19:0, LPE C20:0, propane-1,3-diol) with currently used protein biomarkers like sFlt-1/PlGF will improve PE prediction. Similarly, CE17:1 and CER(d20:1/24:1) alongwith sFlt-1/PlGF makes a better prediction of PE than sFlt-1/PlGF alone A comprehensive map of lipid profiles in early pregnancy may provide an improved understanding of disease pathogenesis and will be useful predictive biomarkers. In this article, we aimed to summarize the significance of lipid metabolism in the preeclampsia pathogenesis and altered lipidome signatures in preeclampsia. We also discuss the future scope of lipidomics in aiding early prediction of PE and future cardiovascular risk in both mother and child.

Paradoxes: Cholesterol and Hypoxia in Preeclampsia

Preeclampsia, a hypertensive disease of pregnancy of unknown etiology, is intensely studied as a model of cardiovascular disease (CVD) not only due to multiple shared pathologic elements but also because changes that develop over decades in CVD appear and resolve within days in preeclampsia. Those affected by preeclampsia and their offspring experience increased lifetime risks of CVD. At the systemic level, preeclampsia is characterized by increased cellular, membrane, and blood levels of cholesterol; however, cholesterol-dependent signaling, such as canonical Wnt/βcatenin, Hedgehog, and endothelial nitric oxide synthase, is downregulated indicating a cholesterol deficit with the upregulation of cholesterol synthesis and efflux. Hypoxia-related signaling in preeclampsia also appears to be paradoxical with increased Hypoxia-Inducible Factors in the placenta but measurably increased oxygen in maternal blood in placental villous spaces. This review addresses the molecular mechanisms by which excessive systemic cholesterol and deficient cholesterol-dependent signaling may arise from the effects of dietary lipid variance and environmental membrane modifiers causing the cellular hypoxia that characterizes preeclampsia.

The Role of Obesity in the Development of Preeclampsia

PURPOSE OF REVIEW

This comprehensive review provides an in-depth exploration of the complex relationship between obesity and preeclampsia (PE) and emphasizes the clinical implications of this association. It highlights the crucial role of screening tools in assessing individual risk and determining the need for additional antenatal care among women with obesity. The review investigates various markers for identifying the risk of developing PE, while emphasizing the significance of interventions such as exercise, weight management, and a balanced diet in reducing the incidence of preeclampsia and improving outcomes for both mother and fetus.

RECENT FINDINGS

Actually, there is a global pandemic of obesity, particularly among women of childbearing age and pregnant women. PE, which is characterized by maternal hypertension, proteinuria, and complications, affects 2-4% of pregnancies worldwide, posing significant risks to maternal and perinatal health. Women with obesity face an elevated risk of developing PE due to the systemic inflammation resulting from excess adiposity, which can adversely affect placental development. Adipose tissue, rich in proinflammatory cytokines and complement proteins, contributes to the pathogenesis of PE by promoting the expression of antiangiogenic factors in the mother. This review emphasizes the need for appropriate screening, interventions, and a holistic approach to reduce the incidence of preeclampsia and enhance maternal-fetal well-being, thus providing valuable insights into the multifaceted association between obesity and PE.

Multiomics Approach Reveals Serum Biomarker Candidates for Congenital Zika Syndrome

The Zika virus (ZIKV) can be vertically transmitted, causing congenital Zika syndrome (CZS) in fetuses. ZIKV infection in early gestational trimesters increases the chances of developing CZS. This syndrome involves several pathologies with a complex diagnosis. In this work, we aim to identify biological processes and molecular pathways related to CZS and propose a series of putative protein and metabolite biomarkers for CZS prognosis in early pregnancy trimesters. We analyzed serum samples of healthy pregnant women and ZIKV-infected pregnant women bearing nonmicrocephalic and microcephalic fetuses. A total of 1090 proteins and 512 metabolites were identified by bottom-up proteomics and untargeted metabolomics, respectively. Univariate and multivariate statistical approaches were applied to find CZS differentially abundant proteins (DAP) and metabolites (DAM). Enrichment analysis (i.e., biological processes and molecular pathways) of the DAP and the DAM allowed us to identify the ECM organization and proteoglycans, amino acid metabolism, and arachidonic acid metabolism as CZS signatures. Five proteins and four metabolites were selected as CZS biomarker candidates. Serum multiomics analysis led us to propose nine putative biomarkers for CZS prognosis with high sensitivity and specificity.

Maternal and Child Health, Non-Communicable Diseases and Metabolites

Mothers influence the health and disease trajectories of their children, particularly during the critical developmental windows of fetal and neonatal life reflecting the gestational-fetal and lactational-neonatal phases. As children grow and develop, they are exposed to various stimuli and insults, such as metabolites, that shape their physiology and metabolism to impact their health. Non-communicable diseases, such as diabetes, cardiovascular disease, cancer and mental illness, have high global prevalence and are increasing in incidence. Non-communicable diseases often overlap with maternal and child health. The maternal milieu shapes progeny outcomes, and some diseases, such as gestational diabetes and preeclampsia, have gestational origins. Metabolite aberrations occur from diets and physiological changes. Differential metabolite profiles can predict the onset of non-communicable diseases and therefore inform prevention and/or better treatment. In mothers and children, understanding the metabolite influence on health and disease can provide insights for maintaining maternal physiology and sustaining optimal progeny health over the life course. The role and interplay of metabolites on physiological systems and signaling pathways in shaping health and disease present opportunities for biomarker discovery and identifying novel therapeutic agents, particularly in the context of maternal and child health, and non-communicable diseases.

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Introduction

Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.

Aim

To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.

Methodology

Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.

Current state

ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.

Future challenges

To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.

Conclusion

The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

A theoretical model of dietary lipid variance as the origin of primary ciliary dysfunction in preeclampsia

Serving as the cell's key interface in communicating with the outside world, primary cilia have emerged as an area of multidisciplinary research interest over the last 2 decades. Although the term "ciliopathy" was first used to describe abnormal cilia caused by gene mutations, recent studies focus on abnormalities of cilia that are found in diseases without clear genetic antecedents, such as obesity, diabetes, cancer, and cardiovascular disease. Preeclampsia, a hypertensive disease of pregnancy, is intensely studied as a model for cardiovascular disease partially due to many shared pathophysiologic elements, but also because changes that develop over decades in cardiovascular disease arise in days with preeclampsia yet resolve rapidly after delivery, thus providing a time-lapse view of the development of cardiovascular pathology. As with genetic primary ciliopathies, preeclampsia affects multiple organ systems. While aspirin delays the onset of preeclampsia, there is no cure other than delivery. The primary etiology of preeclampsia is unknown; however, recent reviews emphasize the fundamental role of abnormal placentation. During normal embryonic development, trophoblastic cells, which arise from the outer layer of the 4-day-old blastocyst, invade the maternal endometrium and establish extensive placental vascular connections between mother and fetus. In primary cilia of trophoblasts, Hedgehog and Wnt/catenin signaling operate upstream of vascular endothelial growth factor to advance placental angiogenesis in a process that is promoted by accessible membrane cholesterol. In preeclampsia, impaired proangiogenic signaling combined with an increase in apoptotic signaling results in shallow invasion and inadequate placental function. Recent studies show primary cilia in preeclampsia to be fewer in number and shortened with functional signaling abnormalities. Presented here is a model that integrates preeclampsia lipidomics and physiology with the molecular mechanisms of liquid-liquid phase separation in model membrane studies and the known changes in human dietary lipids over the last century to explain how changes in dietary lipids might reduce accessible membrane cholesterol and give rise to shortened cilia and defects in angiogenic signaling, which underlie placental dysfunction of preeclampsia. This model offers a possible mechanism for non-genetic dysfunction in cilia and proposes a proof-of-concept study to treat preeclampsia with dietary lipids.

Lipidomics Reveals Elevated Plasmalogens in Women with Obesity Who Develop Preeclampsia

Objective: Preeclampsia (PE) is a prevalent pregnancy disorder worldwide with limited preventative treatments available. Obesity triples the risk for PE, yet only 10% of women with obesity develop PE. The factors that distinguish PE from uncomplicated pregnancies in the context of obesity have not been fully established. Methods: We studied a cohort of women with obesity throughout pregnancy to identify lipid mediators and/or biomarkers of PE. Blood samples were collected at each trimester and analyzed by both targeted lipidomics and standard lipid panels. Individual lipid species were compared by PE status at each trimester, as well as by self-identified race (Black vs. White) and fetal sex. Results: Standard lipid panels and clinical measurements revealed few differences between PE and uncomplicated pregnancies. Targeted lipidomics, however, identified plasmalogen, phosphatidylethanolamine, and free fatty acid species that were elevated in the third trimester of women with PE. Furthermore, race and trimester of pregnancy were considerable sources of plasma lipidomic variation in women with obesity. Conclusions: First and second trimester individual plasma lipid species do not predict the development of PE in obese women. In the third trimester, PE patients have elevated levels of plasmalogens-a class of lipoprotein-associated phospholipids that have been implicated in the response to oxidative stress.

First‐trimester plasma extracellular heat shock proteins levels and risk of preeclampsia

Preeclampsia (PE) occurs annually in 8% of pregnancies. Patients without risk factors represent 10% of these. There are currently no first-trimester biochemical markers that accurately predict PE. An increase in serum 60- and 70-KDa extracellular heat shock proteins (eHsp) has been shown in patients who developed PE at 34 weeks. We sought to determine whether there is a relationship between first-trimester eHsp and the development of PE. This was a prospective cohort study performed at a third level hospital in Mexico City from 2019 to 2020. eHsp levels were measured during the first-trimester ultrasound in singleton pregnancies with no comorbidities. First-trimester eHsp levels and biochemical parameters of organ dysfunction were compared between patients who developed preeclampsia and those who did not. All statistical analyses and model of correlation (r) between eHsp and clinical parameter were performed using bootstrapping R-software. p-values <0.05 were considered significant. The final analysis included 41 patients. PE occurred in 11 cases. eHsp-60 and eHsp-70 were significantly higher at 12 weeks in patients who developed PE (p = 0.001), while eHsp-27 was significantly lower (p = 0.004). Significant differences in first-trimester eHsp concentration suggest that these are possible early biomarkers useful for the prediction of PE.

Toward a new taxonomy of obstetrical disease: improved performance of maternal blood biomarkers for the great obstetrical syndromes when classified according to placental pathology

BACKGROUND

The major challenge for obstetrics is the prediction and prevention of the great obstetrical syndromes. We propose that defining obstetrical diseases by the combination of clinical presentation and disease mechanisms as inferred by placental pathology will aid in the discovery of biomarkers and add specificity to those already known.

OBJECTIVE

To describe the longitudinal profile of placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1), and the PlGF/sFlt-1 ratio throughout gestation, and to determine whether the association between abnormal biomarker profiles and obstetrical syndromes is strengthened by information derived from placental examination, eg, the presence or absence of placental lesions of maternal vascular malperfusion.

STUDY DESIGN

This retrospective case cohort study was based on a parent cohort of 4006 pregnant women enrolled prospectively. The case cohort of 1499 pregnant women included 1000 randomly selected patients from the parent cohort and all additional patients with obstetrical syndromes from the parent cohort. Pregnant women were classified into six groups: 1) term delivery without pregnancy complications (n=540; control); 2) preterm labor and delivery (n=203); 3) preterm premature rupture of the membranes (n=112); 4) preeclampsia (n=230); 5) small-for-gestational-age neonate (n=334); and 6) other pregnancy complications (n=182). Maternal plasma concentrations of PlGF and sFlt-1 were determined by enzyme-linked immunosorbent assays in 7560 longitudinal samples. Placental pathologists, masked to clinical outcomes, diagnosed the presence or absence of placental lesions of maternal vascular malperfusion. Comparisons between mean biomarker concentrations in cases and controls were performed by utilizing longitudinal generalized additive models. Comparisons were made between controls and each obstetrical syndrome with and without subclassifying cases according to the presence or absence of placental lesions of maternal vascular malperfusion.

RESULTS

1) When obstetrical syndromes are classified based on the presence or absence of placental lesions of maternal vascular malperfusion, significant differences in the mean plasma concentrations of PlGF, sFlt-1, and the PlGF/sFlt-1 ratio between cases and controls emerge earlier in gestation; 2) the strength of association between an abnormal PlGF/sFlt-1 ratio and the occurrence of obstetrical syndromes increases when placental lesions of maternal vascular malperfusion are present (adjusted odds ratio [aOR], 13.6 vs 6.7 for preeclampsia; aOR, 8.1 vs 4.4 for small-for-gestational-age neonates; aOR, 5.5 vs 2.1 for preterm premature rupture of the membranes; and aOR, 3.3 vs 2.1 for preterm labor (all P<0.05); and 3) the PlGF/sFlt-1 ratio at 28 to 32 weeks of gestation is abnormal in patients who subsequently delivered due to preterm labor with intact membranes and in those with preterm premature rupture of the membranes if both groups have placental lesions of maternal vascular malperfusion. Such association is not significant in patients with these obstetrical syndromes who do not have placental lesions.

CONCLUSION

Classification of obstetrical syndromes according to the presence or absence of placental lesions of maternal vascular malperfusion allows biomarkers to be informative earlier in gestation and enhances the strength of association between biomarkers and clinical outcomes. We propose that a new taxonomy of obstetrical disorders informed by placental pathology will facilitate the discovery and implementation of biomarkers as well as the prediction and prevention of such disorders.

Dissecting the Roles of Lipids in Preeclampsia

Preeclampsia is a multisystem pregnancy disorder that is characterized by different degrees of placental malperfusion, with release of antiangiogenic factors into the circulation, leading to maternal vascular endothelial injury and high blood pressure. As a major cause of maternal and perinatal mortality and morbidity worldwide, once preeclampsia has been diagnosed, there are no curative treatments except for delivery. Lipids serve as ubiquitous and multifunctional metabolites that are integral and essential to many diverse functions on both a cellular and organismal level. Lipid metabolic abnormalities have emerged as potential risk factors for the development and progression of preeclampsia. This review comprehensively examines decades of discovery to illuminate the roles of lipids and dysregulation in the levels of various lipid classes in preeclampsia. In addition, the roles of lipids are summarized to further understand the pathogenic mechanisms of preeclampsia. Overall, the review highlights the promising potential of pathophysiology and lipid-targeting therapeutic strategies 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.

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.

Optimization of metabolomic data processing using NOREVA

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.

MicroRNA-mRNA Networks in Pregnancy Complications: A Comprehensive Downstream Analysis of Potential Biomarkers

Pregnancy complications are a major cause of fetal and maternal morbidity and mortality in humans. The majority of pregnancy complications initiate due to abnormal placental development and function. During the last decade, the role of microRNAs (miRNAs) in regulating placental and fetal development has become evident. Dysregulation of miRNAs in the placenta not only affects placental development and function, but these miRNAs can also be exported to both maternal and fetal compartments and affect maternal physiology and fetal growth and development. Due to their differential expression in the placenta and maternal circulation during pregnancy complications, miRNAs can be used as diagnostic biomarkers. However, the differential expression of a miRNA in the placenta may not always be reflected in maternal circulation, which makes it difficult to find a reliable biomarker for placental dysfunction. In this review, we provide an overview of differentially expressed miRNAs in the placenta and/or maternal circulation during preeclampsia (PE) and intrauterine growth restriction (IUGR), which can potentially serve as biomarkers for prediction or diagnosis of pregnancy complications. Using different bioinformatics tools, we also identified potential target genes of miRNAs associated with PE and IUGR, and the role of miRNA-mRNA networks in the regulation of important signaling pathways and biological processes.

Prediction of preeclampsia risk in first time pregnant women: Metabolite biomarkers for a clinical test

Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality. Accurate prediction of preeclampsia risk would enable more effective, risk-based prenatal care pathways. Current risk assessment algorithms depend on clinical risk factors largely unavailable for first-time pregnant women. Delivering accurate preeclampsia risk assessment to this cohort of women, therefore requires for novel biomarkers. Here, we evaluated the relevance of metabolite biomarker candidates for their selection into a prototype rapid, quantitative Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) based clinical screening assay. First, a library of targeted LC-MS/MS assays for metabolite biomarker candidates was developed, using a medium-throughput translational metabolomics workflow, to verify biomarker potential in the Screening-for-Pregnancy-Endpoints (SCOPE, European branch) study. A variable pre-selection step was followed by the development of multivariable prediction models for pre-defined clinical use cases, i.e., prediction of preterm preeclampsia risk and of any preeclampsia risk. Within a large set of metabolite biomarker candidates, we confirmed the potential of dilinoleoyl-glycerol and heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine to effectively complement Placental Growth Factor, an established preeclampsia biomarker, for the prediction of preeclampsia risk in first-time pregnancies without overt risk factors. These metabolites will be considered for integration in a prototype rapid, quantitative LC-MS/MS assay, and subsequent validation in an independent cohort.