Dataset Information

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 SM

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Lee Seung Mi SM Kang Yujin Y Lee Eun Mi EM Jung Young Mi YM Hong Subeen S Park Soo Jin SJ Park Chan-Wook CW Norwitz Errol R ER Lee Do Yup DY Park Joong Shin JS

Scientific reports 20200930 1

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 (gestat ...[more]

PMID: 32999354

Similar Datasets

Project description:Patients with systemic lupus erythematosus (SLE) are at increased risk for adverse pregnancy outcome (APO). Accurate prediction of APO is critical to identify, counsel, and manage these high-risk patients. We undertook this study to identify novel biomarkers in mid-trimester maternal plasma to identify pregnant patients with SLE at increased risk of APOs. The study population consisted of pregnant women whose plasma was taken in mid-trimester and available for metabolic signature: (1) SLE and normal pregnancy outcome (Group 1, n = 21); (2) SLE with APO (Group 2, n = 12); and (3) healthy pregnant controls (Group 3, n = 10). Mid-trimester maternal plasma was analyzed for integrative profiles of primary metabolite and phospholipid using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and liquid chromatography Orbitrap mass spectrometry (LC-Orbitrap MS). For performance comparison and validation, plasma samples were analyzed for sFlt-1/PlGF ratio. In the study population, APO developed in 12 of 33 women with SLE (36%). Metabolite profiling of mid-trimester maternal plasma samples identified a total of 327 metabolites using GC-TOF MS and LC-Orbitrap MS. Partial least squares discriminant analysis (PLS-DA) showed clear discrimination among the profiles of SLE groups and healthy pregnant controls (Groups 1/2 vs. 3). Moreover, direct comparison between Groups 1 and 2 demonstrated that 4 primary metabolites and 13 lipid molecules were significantly different. Binary logistic regression analysis suggested a potential metabolic biomarker model that could discriminate Groups 1 and 2. Receiver operating characteristic (ROC) analysis revealed the best predictability for APO with the combination model of two metabolites (LysoPC C22:5 and tryptophan) with AUC of 0.944, comparable to the AUC of sFlt-1/PlGF (AUC 0.857). In conclusion, metabolic biomarkers in mid-trimester maternal plasma can accurately predict APO in patients with SLE.

Project description:Alzheimer's disease biomarkers are widely accepted as surrogate markers of underlying neuropathological changes. However, few studies have evaluated whether preclinical Alzheimer's disease biomarkers predict Alzheimer's neuropathology at autopsy. We sought to determine whether amyloid PET imaging or CSF biomarkers accurately predict cognitive outcomes and Alzheimer's disease neuropathological findings. This study included 720 participants, 42-91 years of age, who were enrolled in longitudinal studies of memory and aging in the Washington University Knight Alzheimer Disease Research Center and were cognitively normal at baseline, underwent amyloid PET imaging and/or CSF collection within 1 year of baseline clinical assessment, and had subsequent clinical follow-up. Cognitive status was assessed longitudinally by Clinical Dementia Rating®. Biomarker status was assessed using predefined cut-offs for amyloid PET imaging or CSF p-tau181/amyloid-β42. Subsequently, 57 participants died and underwent neuropathologic examination. Alzheimer's disease neuropathological changes were assessed using standard criteria. We assessed the predictive value of Alzheimer's disease biomarker status on progression to cognitive impairment and for presence of Alzheimer's disease neuropathological changes. Among cognitively normal participants with positive biomarkers, 34.4% developed cognitive impairment (Clinical Dementia Rating > 0) as compared to 8.4% of those with negative biomarkers. Cox proportional hazards modelling indicated that preclinical Alzheimer's disease biomarker status, APOE ɛ4 carrier status, polygenic risk score and centred age influenced risk of developing cognitive impairment. Among autopsied participants, 90.9% of biomarker-positive participants and 8.6% of biomarker-negative participants had Alzheimer's disease neuropathological changes. Sensitivity was 87.0%, specificity 94.1%, positive predictive value 90.9% and negative predictive value 91.4% for detection of Alzheimer's disease neuropathological changes by preclinical biomarkers. Single CSF and amyloid PET baseline biomarkers were also predictive of Alzheimer's disease neuropathological changes, as well as Thal phase and Braak stage of pathology at autopsy. Biomarker-negative participants who developed cognitive impairment were more likely to exhibit non-Alzheimer's disease pathology at autopsy. The detection of preclinical Alzheimer's disease biomarkers is strongly predictive of future cognitive impairment and accurately predicts presence of Alzheimer's disease neuropathology at autopsy.

Project description:OBJECTIVE:Changes in the maternal plasma concentrations of angiogenic (placental growth factor (PlGF) and vascular endothelial growth factor (VEGF)) and anti-angiogenic factors (sEng and vascular endothelial growth factor receptor-1 (sVEGFR-1)) precede the clinical presentation of preeclampsia. This study was conducted to examine the role of maternal plasma PlGF, sEng, and sVEGFR-1 concentrations in early pregnancy and midtrimester in the identification of patients destined to develop preeclampsia. METHODS:This longitudinal cohort study included 1622 consecutive singleton pregnant women. Plasma samples were obtained in early pregnancy (6-15 weeks) and midtrimester (20-25 weeks). Maternal plasma PlGF, sEng, and sVEGFR-1 concentrations were determined using sensitive and specific immunoassays. The primary outcome was the development of preeclampsia. Secondary outcomes included term, preterm, and early-onset preeclampsia. Receiving operating characteristic curves, sensitivity, specificity, positive and negative likelihood ratios, and multivariable logistic regression were applied. A p-value of <0.05 was considered significant. RESULTS:(1) The prevalence of preeclampsia, term, preterm, (<37 weeks) and early-onset preeclampsia (<34 weeks) was 3.8 (62/1622), 2.5 (40/1622), 1.4 (22/1622) and 0.6% (9/1622), respectively; (2) Higher likelihood ratios were provided by ratios of midtrimester plasma concentrations of PlGF, sEng, and sVEGFR-1 than single analytes; (3) Individual angiogenic and anti-angiogenic factors did not perform well in the identification of preeclampsia as a whole; in particular, they perform poorly in the prediction of term preeclampsia; (4) In contrast, a combination of these analytes such as the PlGF/sEng ratio, its delta and slope had the best predictive performance with a sensitivity of 100%, a specificity of 98-99%, and likelihood ratios for a positive test of 57.6, 55.6 and 89.6, respectively, for predicting early-onset preeclampsia. CONCLUSIONS:(1) The PlGF/sEng ratio and its delta and slope had an excellent predictive performance for the prediction of early-onset preeclampsia, with very high likelihood ratios for a positive test result and very low likelihood ratios for a negative test result; and (2) Although the positive likelihood ratios are high and the positive predictive values low, the number of patients needed to be closely followed is 4:1 for the PlGF/sEng ratio and 3:1 for the slope of PlGF/sEng.

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.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets