Metabolomic Diagnostics is a collaborator in the IMPROvED consortium

5000 first time mothers will be recruited over the course of 2 years to academic medical centres across Europe (Ireland, UK, the Netherlands, Sweden, and Germany), in a phase IIa prognostic multicentre hospital-based clinical study.

7 Recruitment sites (all with high patient-throughput) have been selected on the basis of investigator expertise and background in pre-eclampsia research; the different centres will facilitate assessment of the screening test in different models of healthcare delivery.

Pertinent and detailed clinical data will be collected, and blood samples taken in the first trimester, at 15 and at 20 weeks’ gestation. Women will then be followed throughout their pregnancies and pertinent outcomes will be recorded.


Establishing a high calibre biobank, augmented by accurate clinical metadata, will enable the development of predictive screening tests for pre-eclampsia and will also provide a vital resource for pregnancy researchers across Europe.

MedSciNet will deliver a sophisticated web-based informatics platform, already widely used internationally for data management in clinical trials and cohort studies, to create biobank management software augmented with clinical metadata.


The IMPROvED programme will create a cadre of highly trained midwives, clinical academics, PhD students and postdoctoral fellows, who will be accustomed to working across national and disciplinary boundaries. There are three key elements to the comprehensive training initiative of the IMPROvED programme:

Research midwives: will undergo extensive training in clinical data collection, database management, phenotypic assessment, sample pro-cessing and biobank curation. Much of these training will be condensed into an intensive three month period prior to the initiation of patient recruitment. However, workshops will continue through the duration of the programme. Midwives employed through the IMPROvED programme will be encouraged to register for higher research qualifications (such as Masters in Research Methodology).
Clinical academics: Each of the six academic recruiting partners will identify a junior clinical academic who will undergo training in all aspects of our multidisciplinary, multicentre, hospital-based study. Each junior clinical academic will be mentored by one of the senior investigators, and will be involved in every facet of the study, from design, through to analysis of samples and interpretation of results. They will be involved in the ethical discussions, interact with patient groups, and spend time with SME partners.
PhD students and postdoctoral fellows: several PhD students and postdoctoral fellows will be employed and trained through the IMPROvED programme. Although each will be working on specific, focused projects, they will be co-supervised by investigators from different centres and different disciplines. For example, a PhD student will be employed between UCC and KI, and, utilizing the IMPROvED biobank and database, will search for and validate biomarkers for spontaneous pre-term birth (incidence of 9% as with equally compelling health and economic arguments for screening). These students and fellows will receive high calibre scientific training but will also facilitate enhanced synergy and collaboration between partners.


Robust Early Pregnancy Prediction of Later Preeclampsia Using Metabolomic Biomarkers

Preeclampsia is a pregnancy-specific syndrome that causes substantial maternal and fetal morbidity and mortality. The etiology is incompletely understood, and there is no clinically useful screening test. Current metabolomic technologies have allowed the establishment of metabolic signatures of preeclampsia in early pregnancy. Here, a 2-phase discovery/validation metabolic profiling study was performed. In the discovery phase, a nested case-control study was designed, using samples obtained at 15 weeks’ gestation from 60 women who subsequently developed preeclampsia and 60 controls taking part in the prospective Screening for Pregnancy Endpoints cohort study. Controls were proportionally population matched for age, ethnicity, and body mass index at booking. Plasma samples were analyzed using ultra performance liquid chromatography-mass spectrometry. A multivariate predictive model combining 14 metabolites gave an odds ratio for developing preeclampsia of 36 (95% CI: 12 to 108), with an area under the receiver operator characteristic curve of 0.94. These findings were then validated using an independent case-control study on plasma obtained at 15_1 weeks from 39 women who subsequently developed preeclampsia and 40 similarly matched controls from a participating center in a different country. The same 14 metabolites produced an odds ratio of 23 (95% CI: 7 to 73) with an area under receiver operator characteristic curve of 0.92. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of preeclampsia offers insight into disease pathogenesis and offers the tantalizing promise of a robust presymptomatic screening test.

The full paper is available here



Metabolic Profiling Uncovers a Phenotypic Signature of Small for Gestational Age in Early Pregnancy

Richard P Horgan, David I. Broadhurst, Sarah K. Walsh, Warwick B. Dunn, Marie Brown, Claire T. Roberts, Robyn A. North, Lesley M. McCowan, Douglas B. Kell, Philip N. Baker, and Louise C. Kenny

‘Being born small for gestational age (SGA) confers increased risks of perinatal morbidity and mortality and increases the risk of cardiovascular complications and diabetes in later life. Accumulating evidence suggests that the etiology of SGA is usually associated with poor placental vascular development in early pregnancy. We examined metabolomic profiles using ultra performance liquid chromatography–mass spectrometry (UPLC–MS) in three independent studies: (a) venous cord plasma from normal and SGA babies, (b) plasma from a rat model of placental insufficiency and controls, and (c) early pregnancy peripheral plasma samples from women who subsequently delivered a SGA baby and controls. Multivariate analysis by cross-validated Partial Least Squares Discriminant Analysis (PLS-DA) of all 3 studies showed a comprehensive and similar disruption of plasma metabolism. A multivariate predictive model combining 19 metabolites produced by a Genetic Algorithm-based search program gave an Odds Ratio for developing SGA of 44, with an area under the Receiver Operator Characteristic curve of 0.9. Sphingolipids, phospholipids, carnitines, and fatty acids were among this panel of metabolites. The finding of a consistent discriminatory metabolite signature in early pregnancy plasma preceding the onset of SGA offers insight into disease pathogenesis and offers the promise of a robust presymptomatic screening test.’

The full paper is available here.