Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning

Abstract

Pre-eclampsia is a multi-system disorder of pregnancy with major maternal and perinatal implications. Emerging therapeutic strategies are most likely to be maximally effective if commenced weeks or even months prior to the clinical presentation of the disease. Although widespread plasma alterations precede the clinical onset of pre-eclampsia, no single plasma constituent has emerged as a sensitive or specific predictor of risk. Consequently, currently available methods of identifying the condition prior to clinical presentation are of limited clinical use. We have exploited genetic programming, a powerful data mining method, to identify patterns of metabolites that distinguish plasma from patients with pre-eclampsia from that taken from healthy, matched controls. High-resolution gas chromatography time-of-flight mass spectrometry (GC-tof-MS) was performed on 87 plasma samples from women with pre-eclampsia and 87 matched controls. Normalised peak intensity data were fed into the Genetic Programming (GP) system which was set up to produce a model that gave an output of 1 for patients and 0 for controls. The model was trained on 50% of the data generated and tested on a separate hold-out set of 50%. The model generated by GP from the GC-tof-MS data identified a metabolomic pattern that could be used to produce two simple rules that together discriminate pre-eclampsia from normal pregnant controls using just 3 of the metabolite peak variables, with a sensitivity of 100% and a specificity of 98%. Thus, preeclampsia can be diagnosed at the level of small-molecule metabolism in blood plasma. These findings justify a prospective assessment of metabolomic technology as a screening tool for pre-eclampsia, while identification of the metabolites involved may lead to an improved understanding of the aetiological basis of pre-eclampsia and thus the development of targeted therapies.

Metabolomic mapping of atypical antipsychotic effects in schizophrenia

“…[13][14][15][16][17][18][19][20] Lipidomics is a branch of metabolomics where focus is specifically targeted on quantifying a wide range of polar and nonpolar lipid metabolites enabling a more comprehensive assessment of human lipid biochemical pathways than was previously practical. [21][22][23] We used this platform to map and compare global lipid changes in schizophrenia and upon treatment with three commonly used atypical antipsychotic drugs.…”

Robust Early Pregnancy Prediction of Later Preeclampsia Using Metabolomic Biomarkers

“…10 -12 We previously reported results of an anonymous metabolomic screen of plasma from women with established PE. 13,14 Subsequently, we identified highly discriminatory metabo- …”

Systems biology, metabolic modelling and metabolomics in drug discovery and development

“…Thus, following the development of the 'Robot Scientist' approach [44], in which closed-loop methods of active learning are exploited to design and perform serial experiments in an intelligent manner, a comparable strategy has been exploited to increase hugely the number of metabolites that can be detected chromatographically. Using one-dimensional GC-tof-MS, the number of peaks observable in human serum was approximately trebled (compared with another GC-tof-MS method that was the starting point and that we thereby improved) to >1200 [45], which has already enabled the discovery of several novel biomarkers for pre-eclampsia [46] (and see later). Two-dimensional GCxGC-tof-MS has been exploited further to treble this number to >4000 raw peaks, which equates to 1800 metabolite peaks [47,48].…”

“…Furthermore, changes in six metabolites (citric acid, uric acid and GABA plus three unidentified metabolites) differentiated cases from controls with a high degree of accuracy. Using gas-chromatography-mass spectrometry [46] (Kenny, L.C., Dunn, W.B., Broadhurst, D.I., Brown, M.C., Ellis, D.I., Myers, J., Baker, P.N., The GOPEC Consortium and D.B. Kell, unpublished), a series of novel small MW metabolites have been discovered that serve as diagnostic biomarkers for pre-eclampsia (Figure 3).…”