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

ABSTRACT: 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.

SUBMITTER: Lee S

PROVIDER: S-EPMC7527521 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Project description:Hypertension in pregnancy is the leading cause of morbidity and mortality in pregnancy, affecting up to 10% of all gestations1. Hypertensive disorders of pregnancy include chronic hypertension, gestational hypertension, eclampsia and pre-eclampsia, all of which increase the risk of complications in both mothers and babies during gestation. Preeclampsia, in particular, is characterised by the new-onset of gestational hypertension in the presence of proteinuria or other organ damage. It affects 5-7% of pregnancies and causes approximately 76,000 maternal deaths and 500,000 foetal deaths worldwide each year2. The classification of preeclampsia has been evolving over the last decade. In 2013, American College of Obstetricians and Gynecologists (ACOG) and International Society for the Study of Hypertension in Pregnancy (ISSHP) in the absence of proteinuria included other symptoms/features including liver dysfunction, thrombocytopenia, cerebrovascular events or foetal growth restriction (FGR) in the diagnosis of preeclampsia 1,3,4. Despite the fact that preeclampsia is a multifactorial and heterogeneous disorder, it is now widely accepted that preeclampsia is stratified depending on the time of onset into: i) early-onset PE (EOPE) manifested before 34 weeks of gestation, and ii) late-onset PE (LOPE) manifested from 34 weeks of gestation. Although EOPE and LOPE share the same diagnostic criteria, these two phenotypes of preeclampsia lead to different outcomes. EOPE is commonly associated with FGR, abnormal uterine artery Doppler often leading to preterm birth and higher risk of post-pregnancy morbidities5,6. On the other hand, LOPE appears to be a less severe disorder, often with normal or slightly increased uterine resistance index and a low rate of FGR6,7. Distinct delineation between EOPE and LOPE is still not well understood, with most patients with preeclampsia presenting elements of both pathologies proposing a clinical spectrum for preeclampsia. The lack of untargeted discovery studies involving ‘omics’ analyses has impeded understanding of the molecular differences between these two phenotypes of preeclampsia. Notably, a study from 20058 utilised a proteomics approach using urine samples from a cohort of pregnant women with EOPE and healthy controls, however the differences between EOPE and LOPE were not elucidated. Another more recent bioinformatics study, identified overlapping pathogenic mechanisms between preeclampsia, hypertension and heart disease but could not stratify between EOPE and LOPE due to underreported of data related to individual phenotypes9. In this study, we conducted an unbiased, comprehensive proteomics investigation using plasma samples collected from patients with EOPE (n=17) and LOPE (n=11), compared with age- and BMI-matched normotensive controls (n=18). The use of plasma samples is able to better reflect the pathogenesis of EOPE and LOPE as systemic conditions centred by widespread endothelial dysfunction.

Project description:BackgroundA well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that represent maternal-fetal interface tissues for predicting preeclampsia but not COVID-19 infection.MethodsThe surrogate transcriptome of tissues was determined by that in maternal blood, utilizing four datasets (n = 1354) which were collected before the COVID-19 pandemic. Applying machine learning, a preeclampsia prediction model was chosen between those using blood transcriptome (differentially expressed genes [DEGs]) and the blood-derived surrogate for tissues. We selected the best predictive model by the area under the receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets both with and without an intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but that were not positive in the COVID-19 dataset (n = 47), we compared several methods of predictor discovery: (1) the best prediction model; (2) gene sets of standard pipelines; and (3) a validated gene set for predicting any-onset preeclampsia during the pandemic (n = 404). We chose the most predictive biomarkers from the best method with the significantly largest number of discoveries by a permutation test. The biological relevance was justified by exploring and reanalyzing low- and high-level, multiomics information.ResultsA prediction model using the surrogates developed for predicting any-onset preeclampsia (AUROC of 0.85, 95 % confidence interval [CI] 0.77 to 0.93) was the only that was well-replicated in an independent dataset with no intervention. No model was well-replicated in datasets with a vitamin D intervention. None of the blood biomarkers with high weights in the best model overlapped with blood DEGs. Blood biomarkers were transcripts of integrin-α5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered eligible blood biomarkers (n = 3/100 combinations, 3.0 %; P =.036). Most of the predicted events (73.70 %) among any-onset preeclampsia were cluster A as defined by ITGA5 (Z-score ≥ 1.1), but were only a minority (6.34 %) among positives in the COVID-19 dataset. The remaining were predicted events (26.30 %) among any-onset preeclampsia or those among COVID-19 infection (93.66 %) if IRF6 Z-score was ≥-0.73 (clusters B and C), in which none was the predicted events among either late-onset preeclampsia (LOPE) or COVID-19 infection if P2RX7 Z-score was <0.13 (cluster C). Greater proportions of predicted events among LOPE were cluster A (82.85 % vs 70.53 %) compared to early-onset preeclampsia (EOPE). The biological relevance by multiomics information explained the biomarker mechanism, polymicrobial infection in any-onset preeclampsia by ITGA5, viral co-infection in EOPE by ITGA5-IRF6, a shared prediction with COVID-19 infection by ITGA5-IRF6-P2RX7, and non-replicability in datasets with a vitamin D intervention by ITGA5.ConclusionsIn a model that predicts preeclampsia but not COVID-19 infection, the important predictors were genes in maternal blood that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.

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

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