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Prediction of preeclampsia risk is key to informing effective maternal care. Current screening for preeclampsia at 11 -13 weeks' gestation using maternal demographic characteristics and medical history with measurements of mean arterial pressure, uterine artery pulsatility index and serum placental growth factor can identify about 75% of women who develop preterm preeclampsia with delivery at37 weeks' gestation. Further improvements to preeclampsia screening tests will likely require integrating additional biomarkers. Recent research suggests the existence of distinct maternal risk profiles. Biomarker evaluation should therefore account for the possibility that a biomarker only predicts preeclampsia in a specific maternal phenotype.Verification of metabolite biomarkers as preterm preeclampsia predictors early in pregnancy in all women and across body mass index (BMI) groups.Observational case-control study drawn from a large prospective study on the early prediction of pregnancy complications in women attending their routine first hospital visit at King's College Hospital, London, UK, in 2010-2015. Pregnant women underwent a complete first-trimester assessment, including the collection of blood samples for biobanking. In 11-13 weeks' plasma samples of 2501 singleton pregnancies levels of pre-selected metabolites implicated in the prediction of pregnancy complications were determined using a targeted liquid chromatography-mass spectrometry method, yielding high-quality quantification data on 50 metabolites. Ratios of amino acid levels involved in arginine biosynthesis and nitric oxide synthase pathways were added to the list of biomarkers. Placental growth factor and pregnancy-associated plasma protein-A were also available for all study subjects, serving as comparator risk predictors. Data on 1635 control and 106 preterm preeclampsia pregnancies were considered for this analysis, normalized using multiples of medians. Prediction analyses were performed across the following patient strata: all subjects, and the BMI classes25 kg/mLevels of 13 metabolites were associated with preterm preeclampsia in the entire study population (p0.05) with particularly significant associations found for five of them, namely 2-hydroxy-(2/3)-methylbutyric acid, 25-hydroxyvitamin D3, 2-hydroxybutyric acid, alanine, dodecanoylcarnitine and 1-(1Z-octadecenyl)-2-oleoyl-sn-glycero-3-phosphocholine (p0.01). Fold changes in seven amino acid ratios, all involving glutamine or ornithine, were also significantly different between cases and controls (p0.01). The predictive performance of some metabolites and ratios differed according to BMI classification; for example, ornithine (p0.001) and several ornithine-related ratios (p0.0001 - p0.01) were only strongly associated with preterm preeclampsia in the BMI25 kg/mSingle metabolites and ratios of amino acids related to arginine bioavailability and nitric oxide synthase pathways were associated with preterm preeclampsia risk at 11 -13 weeks' gestation. Differential prediction was observed across BMI classes, supporting the existence of distinct maternal risk profiles. Future studies in preeclampsia prediction should account for the possibility of different maternal risk profiles to improve etiological and prognostic understanding and, ultimately, clinical utility of screening tests.

The present study involved both human cohorts and animal experiments to explore the performance of soluble CD146 (sCD146), a marker of endothelial function, as a diagnostic marker of acutely decompensated heart failure (ADHF), to determine the influence of patients' characteristics on that performance and to explore the potential application of CD146 in the pathophysiology of ADHF.NT-proBNP and sCD146 were measured in three hundred ninety-one patients admitted to the emergency department for acute dyspnea. ROC curve analysis demonstrated that AUCs for ADHF diagnosis in dyspneic patients were 0.86 (95% CI: 0.82-0.90) for sCD146 and 0.90 (95% CI: 0.86-0.92) for NT-proBNP. Subgroup analyses demonstrated that adding sCD146 to NT-proBNP improved the diagnostic performance for patients lying in the gray zone of NT-proBNP (p=0.02) and could be especially useful for ruling-out ADHF. An experimental model of ADHF in rats using thoracic aortic constriction suggests that CD146 is expressed in the intima of large arteries and associated with both left ventricular function and organ congestion.sCD146, a marker of endothelial function, seems to be as powerful as NT-proBNP is used to detect the cardiac origin of an acute dyspnea. The combination of sCD146 and NT-proBNP may have better performance than NT-proBNP used alone in particular for patients underlying in the "gray" zone and could therefore be an improved option for ruling-out ADHF. Both experimental and human data suggest that CD146 is related to systolic left ventricular function and to organ congestion.

A novel gel-free proteomic technology was used to identify more than 800 proteins from 50 million Escherichia coli K12 cells in a single analysis. A peptide mixture is first obtained from a total unfractionated cell lysate, and only the methionine-containing peptides are isolated and identified by mass spectrometry and database searching. The sorting procedure is based on the concept of diagonal chromatography but adapted for highly complex mixtures. Statistical analysis predicts that we have identified more than 40% of the expressed proteome, including soluble and membrane-bound proteins. Next to highly abundant proteins, we also detected low copy number components such as the E. coli lactose operon repressor, illustrating the high dynamic range. The method is about 100 times more sensitive than two-dimensional gel-based methods and is fully automated. The strongest point, however, is the flexibility in the peptide sorting chemistry, which may target the technique toward quantitative proteomics of virtually every class of peptides containing modifiable amino acids, such as phosphopeptides, amino-terminal peptides, etc., adding a new dimension to future proteome research.

Introduction Screening for pre-eclampsia is a focus of prenatal care and is largely based on the use of clinical risk factors. However, current screening protocols are unfit to determine pre-eclampsia risk in 1st time pregnant women; biomarkers have the potential to address this unmet need. As single biomarkers have insufficient predictive accuracy, unbiased biomarker discoveries have been performed to identify panels of markers which when combined, have the potential pre-eclampsia prediction. Metabolite based solutions using mass spectrometry have gained significant interest as they have the potential to readily translate to clinical practice. Objectives To translate the discovery that blood-borne metabolite biomarkers stratify pregnant women early in pregnancy (∼15 weeks) to their risk of pre-eclampsia [1] into a commercial LC–MS based clinical assay. To pursue fit-for-purpose testing of the first version of the developed LC–MS pipeline followed by independent verification through a case:control study. Methods The analysis pipeline incorporated (1) a single step metabolite extraction, (2) multiplex LC-QqQ-MS assays for 40+ metabolites and (3) a dedicated data processing protocol. Case:control study testing utilised 15 weeks’ plasma samples of from 50 pregnant women who subsequently developed pre-eclampsia and 500 random control pregnancies. All participants are part of the SCOPE study [2] and were recruited in Cork, Ireland. Results For the 40+ metabolite assays: 62% had a %CV £ 15% and 82% had a %CV £ 25%. Univariate analyses using the ROC statistic showed that 7 of the metabolites tested had significant predictive power (lower limit 95% CI ROC-AUC [3] 0.5). Multivariate logistic regression analysis revealed particular combinations of metabolites which identified groups of women either at increased risk or at decreased risk for pre-eclampsia. Conclusion These findings underpin the potential of metabolite-centric multimarker panels to encapsulate a complex syndrome such as pre-eclampsia considerably in advance of any clinical manifestation. Further development steps will include performing additional case:control studies and subsequent clinical validation of this metabolite based test in the large scale European, multicentre phase IIa clinical study IMproved Pregnancy Outcomes by Early Detection (IMPROvED) which is currently recruiting 1st time pregnant women in 5 European countries [3] . Acknowledgment The authors gratefully acknowledge funding from the EU-HEALTH Project IMPROvED (305169) of the Seventh Framework Programme (FP7).

Introduction Currently no test accurately predicts pre-eclampsia (PE) in a healthy nulliparous population. Unbiased protein biomarker discovery has the potential to identify novel markers but multimarker panels are required to achieve clinically relevant prediction of PE. To this purpose, single biomarker performances were obtained and multimarker panels developed in a significant subcohort of the international Screening fOr Pregnancy Endpoints study (SCOPE) study [1]. Objectives To identify and validate novel protein markers for PE prediction using chromatographic and mass spectrometric techniques which enable the identification and quantification of plasma proteins present in plasma at sub ng/ml concentration (Pronota, Belgium). Methods Pre-disease plasma samples (22–26 weeks) from women who subsequently developed PE and those with uncomplicated pregnancies [2] were used to generate 30 plasma proteome profiles using the MASStermind™ pipeline. A set of novel protein candidates were validated using an antibody-free mass spectrometry method using multiple reaction monitoring (MASSterclass™) in a subcohort of the SCOPE study (NZ & Aus) [1]. Relative abundance of 40+ proteins was determined in 20week plasma samples from 100 women who developed PE and 200 women who did not develop PE (included women with other pregnancy complications). Multivariate analyses were performed to identify algorithms with predictive performance using combinations confined to a maximum of 6 parameters (protein markers and clinical parameters) to avoid overfitting. Validation of the prediction panels was performed in an independent subcohort of SCOPE (Europe) comprising 50 PE and 150 no PE. Results From this large scale biomarker discovery effort a number of key results were obtained: a novel protein, i.e., Insulin-like growth factor binding protein, acid labile subunit (IGFALS), was identified. AUC for this marker for the prediction of all PE was 0.71 (CI 0.68–0.75) which was greater than both PlGF and s-Eng (respective AUCs: 0.64 and 0.61). IGFALS was also found to have predictive value for term (AUC 0.70) as well as preterm disease (AUC 0.75). Using multivariate analysis, marker panels were identified that achieved clinically relevant prediction (exemplary panel prediction of all PE cases AUC=0.79; prediction of preterm PE AUC=0.92). These multivariate models were successfully validated in the European SCOPE subcohort. In addition, predictive algorithms based on mass spectrometric read outs were largely invariant to interchanging the IGFALS mass spectrometry quantitation data with IGFALS ELISA data. Conclusion This study demonstrates the capability of high level LC-MS technologies to discover candidate biomarkers and execute large scale multiplex validation to develop a predictive screening test for preeclampsia.