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Metabolic Biomarkers In Midtrimester Maternal Plasma Can Accurately Predict Adverse Pregnancy Outcome in Patients with SLE.

ABSTRACT: 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.

SUBMITTER: Lee SM

PROVIDER: S-EPMC6811572 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Metabolic Biomarkers In Midtrimester Maternal Plasma Can Accurately Predict Adverse Pregnancy Outcome in Patients with SLE.

Lee Seung Mi SM Lee Eun Mi EM Park Jin Kyun JK Jeon Hae Sun HS Oh Sohee S Hong Subeen S Jung Young Mi YM Kim Byoung Jae BJ Kim Sun Min SM Norwitz Errol R ER Lee Eun Bong EB Louangsenlath Souphaphone S Park Chan-Wook CW Jun Jong Kwan JK Park Joong Shin JS Lee Do Yup DY

Scientific reports 20191023 1

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 no ...[more]

PMID: 31645572

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Project description: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.

Project description:BackgroundEffectively predicting the risk of adverse pregnancy outcome (APO) in women with systemic lupus erythematosus (SLE) during early and mid-pregnancy is a challenge. This study was aimed to identify potential markers for early prediction of APO risk in women with SLE.MethodsThe GSE108497 gene expression dataset containing 120 samples (36 patients, 84 controls) was downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed, and differentially expressed genes (DEGs) were screened to define candidate APO marker genes. Next, three individual machine learning methods, random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator, were combined to identify feature genes from the APO candidate set. The predictive performance of feature genes for APO risk was assessed using area under the receiver operating characteristic curve (AUC) and calibration curves. The potential functions of these feature genes were finally analyzed by conventional gene set enrichment analysis and CIBERSORT algorithm analysis.ResultsWe identified 321 significantly up-regulated genes and 307 down-regulated genes between patients and controls, along with 181 potential functionally associated genes in the WGCNA analysis. By integrating these results, we revealed 70 APO candidate genes. Three feature genes, SEZ6, NRAD1, and LPAR4, were identified by machine learning methods. Of these, SEZ6 (AUC = 0.753) showed the highest in-sample predictive performance for APO risk in pregnant women with SLE, followed by NRAD1 (AUC = 0.694) and LPAR4 (AUC = 0.654). After performing leave-one-out cross validation, corresponding AUCs for SEZ6, NRAD1, and LPAR4 were 0.731, 0.668, and 0.626, respectively. Moreover, CIBERSORT analysis showed a positive correlation between regulatory T cell levels and SEZ6 expression (P < 0.01), along with a negative correlation between M2 macrophages levels and LPAR4 expression (P < 0.01).ConclusionsOur preliminary findings suggested that SEZ6, NRAD1, and LPAR4 might represent the useful genetic biomarkers for predicting APO risk during early and mid-pregnancy in women with SLE, and enhanced our understanding of the origins of pregnancy complications in pregnant women with SLE. However, further validation was required.

Project description:BackgroundDifferentiating between a normal intrauterine pregnancy (IUP) and abnormal conditions including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge in early pregnancy. Currently, serial β-human chorionic gonadotropin (β-hCG) and progesterone are the most commonly used plasma biomarkers for evaluating pregnancy prognosis when ultrasound is inconclusive. However, neither biomarker can predict an EP with sufficient and reproducible accuracy. Hence, identification of new plasma biomarkers that can accurately diagnose EP would have great clinical value.MethodsPlasma was collected from a discovery cohort of 48 consenting women having an IUP, EPL, or EP. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by a label-free proteomics analysis to identify significant changes between pregnancy outcomes. A panel of 14 candidate biomarkers were then verified in an independent cohort of 74 women using absolute quantitation by targeted parallel reaction monitoring mass spectrometry (PRM-MS) which provided the capacity to distinguish between closely related protein isoforms. Logistic regression and Lasso feature selection were used to evaluate the performance of individual biomarkers and panels of multiple biomarkers to predict EP.ResultsA total of 1391 proteins were identified in an unbiased plasma proteome discovery. A number of significant changes (FDR ≤ 5%) were identified when comparing EP vs. non-EP (IUP + EPL). Next, 14 candidate biomarkers (ADAM12, CGA, CGB, ISM2, NOTUM, PAEP, PAPPA, PSG1, PSG2, PSG3, PSG9, PSG11, PSG6/9, and PSG8/1) were verified as being significantly different between EP and non-EP in an independent cohort (FDR ≤ 5%). Using logistic regression models, a risk score for EP was calculated for each subject, and four multiple biomarker logistic models were identified that performed similarly and had higher AUCs than models with single predictors.ConclusionsOverall, four multivariable logistic models were identified that had significantly better prediction of having EP than those logistic models with single biomarkers. Model 4 (NOTUM, PAEP, PAPPA, ADAM12) had the highest AUC (0.987) and accuracy (96%). However, because the models are statistically similar, all markers in the four models and other highly correlated markers should be considered in further validation studies.

Project description:Aims/hypothesisMaternal type 2 diabetes during pregnancy and gestational diabetes are associated with childhood adiposity; however, associations of lower maternal glucose levels during pregnancy with childhood adiposity, independent of maternal BMI, remain less clear. The objective was to examine associations of maternal glucose levels during pregnancy with childhood adiposity in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) cohort.MethodsThe HAPO Study was an observational epidemiological international multi-ethnic investigation that established strong associations of glucose levels during pregnancy with multiple adverse perinatal outcomes. The HAPO Follow-up Study (HAPO FUS) included 4832 children from ten HAPO centres whose mothers had a 75 g OGTT at ~28 weeks gestation 10-14 years earlier, with glucose values blinded to participants and clinical caregivers. The primary outcome was child adiposity, including: (1) being overweight/obese according to sex- and age-specific cut-offs based on the International Obesity Task Force (IOTF) criteria; (2) IOTF-defined obesity only; and (3) measurements >85th percentile for sum of skinfolds, waist circumference and per cent body fat. Primary predictors were maternal OGTT and HbA1c values during pregnancy.ResultsFully adjusted models that included maternal BMI at pregnancy OGTT indicated positive associations between maternal glucose predictors and child adiposity outcomes. For one SD difference in pregnancy glucose and HbA1c measures, ORs for each child adiposity outcome were in the range of 1.05-1.16 for maternal fasting glucose, 1.11-1.19 for 1 h glucose, 1.09-1.21 for 2 h glucose and 1.12-1.21 for HbA1c. Associations were significant, except for associations of maternal fasting glucose with offspring being overweight/obese or having waist circumference >85th percentile. Linearity was confirmed in all adjusted models. Exploratory sex-specific analyses indicated generally consistent associations for boys and girls.Conclusions/interpretationExposure to higher levels of glucose in utero is independently associated with childhood adiposity, including being overweight/obese, obesity, skinfold thickness, per cent body fat and waist circumference. Glucose levels less than those diagnostic of diabetes are associated with greater childhood adiposity; this may have implications for long-term metabolic health.

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.

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Metabolic Biomarkers In Midtrimester Maternal Plasma Can Accurately Predict Adverse Pregnancy Outcome in Patients with SLE.

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Metabolic Biomarkers In Midtrimester Maternal Plasma Can Accurately Predict Adverse Pregnancy Outcome in Patients with SLE.

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