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, pre-eclampsia 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.
Similar content being viewed by others
References
Allen J., Davey H.M., Broadhurst D., Rowland J.J., Oliver S.G. and Kell D.B. (2004) Discrimination of the modes of action of antifungal substances by use of metabolic footprinting. Appl. Env. Micr. 70, 6157–6165
Bino R.J., Hall R.D., Fiehn O., Kopka J., Saito K., Draper J., Nikolau B.J., Mendes P., Roessner-Tunali U., Beale M.H., Trethewey R.N., Lange B.M., Wurtele E.S. and Sumner L.W. (2004) Potential of metabolomics as a functional genomics tool. Trends Plant Sci 9, 418–425
Breiman L. (2001) Statistical modeling: The two cultures. Stat. Sci. 16, 199–215
Brindle J.T., Antti H., Holmes E., Tranter G., Nicholson J.K., Bethell H.W., Clarke S., Schofield P.M., McKilligin E., Mosedale D.E. and Grainger D.J. (2002) Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med 8, 1439–1444
Brown M., Dunn W.B., Ellis D.I., Goodacre R., Handl J., Knowles J.D., O’Hagan S., Spasic I. and Kell D.B. (2005) A metabolome pipeline: from concept to data to knowledge. Metabolomics 1, 35–46
CESDI (1998) Confidential Enquiry into stillbirths and deaths in infancy. 5th Annual Report. Maternal and Child Health Research Consortium
Dunn W.B. and Ellis D.I. (2005) Metabolomics: current analytical platforms and methodologies. Trends Anal Chem 24, 285–294
Dunn W.B., Bailey N.J.C. and Johnson H.E. (2005) Measuring the metabolome: current analytical technologies. Analyst 130, 606–625
Ellis D.I., Broadhurst D., Kell D.B., Rowland J.J. and Goodacre R. (2002) Rapid and quantitative detection of the microbial spoilage of meat by Fourier transform infrared spectroscopy and machine learning. Appl Environ Microbiol 68, 2822–2828
Ellis D.I., Harrigan G.G. and Goodacre R. (2003) Metabolic Fingerprinting with Fourier Transform Infrared Spectroscopy. In: Harrigan G.G. and Goodacre R. (eds), Metabolic Profiling: Its role in Biomarker Discovery and Gene Function Analysis. Kluwer Academic, Boston
Fiehn O., Kopka J., Dormann P., Altmann T., Trethewey R.N. and Willmitzer L. (2000) Metabolite profiling for plant functional genomics. Nat Biotechnol 18, 1157–1161
Frank R. and Hargreaves R. (2003) Clinical biomarkers in drug discovery and development. Nat Rev Drug Discov 2, 566–580
Gardosi J. (1998) The application of individualised fetal growth curves. J Perinat Med 26, 137–142
Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D. and Lander E.S. (1999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531–537
Goodacre R., Vaidyanathan S., Dunn W.B., Harrigan G.G. and Kell D.B. (2004) Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol 22, 245–252
van der Greef J., Stroobant P. and van der Heijden R. (2004) The role of analytical sciences in medical systems biology. Curr Opin Chem Biol 8, 559–565
Harrigan G.G. and Goodacre R. (eds), (2003) Metabolic profiling: its role in biomarker discovery and gene function analysis. Kluwer Academic Publishers, Boston
Hayman R., Brockelsby J., Kenny L. and Baker P. (1999) Preeclampsia: the endothelium, circulating factor(s) and vascular endothelial growth factor. J Soc Gynecol Investig 6, 3–10
Hibbard B. and Milner D. (1994) Reports on confidential enquiries into maternal deaths: an audit of previous recommendations. Health Trends 26, 26–28
Jellum E., Bjornson I., Nesbakken R., Johansson E. and Wold S. (1981) Classification of human cancer cells by means of capillary gas chromatography and pattern recognition analysis. J Chromatogr 217, 231–237
Kell D.B. (2002) Genotype:phenotype mapping: genes as computer programs. Trends Genet 18, 555–559
Kell D.B. (2004) Metabolomics and systems biology: making sense of the soup. Curr Opin Microbiol 7, 296–307
Kell, D.B. (2005) Metabolomics, machine learning and modelling: towards an understanding of the language of cells. Biochem Soc Trans 33, in press
Kell D.B. and Mendes P. (2000) Snapshots of systems:metabolic control analysis and biotechnology in the post-genomic era. In: Cornish-Brown A. and Cárdenas M.L. (eds), Technological and Medical Implications of Metabolic Control Analysis. Kluwer Academic, Boston, pp. 3–25
Kell D.B. and Oliver S.G. (2004) Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bioessays 26, 99–105
Kell D.B., Darby R.M. and Draper J. (2001) Genomic computing: explanatory analysis of plant expression profiling data using machine learning. Plant Physiol 126, 943–951
Kell D.B., Brown M., Davey H.M., Dunn W.B., Spasic I. and Oliver S.G. (2005) Metabolic footprinting and Systems Biology: the medium is the message. Nat Rev Microbiol, July issue, in press
Kenny L.C., Baker P.N., Kendall D.A., Randall M.D. and Dunn W.R. (2002) Differential mechanisms of endothelium-dependent vasodilator responses in human myometrial small arteries in normal pregnancy and pre-eclampsia. Clin Sci (Lond) 103, 67–73
Koza J.R. (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA
Lesko L.J. and Atkinson A.J., Jr. (2001) Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies. Annu Rev Pharmacol Toxicol 41, 347–366
Lewis G. (2001) Why women die. Report on Confidential Enquiries into Maternal Deaths in the United Kingdom 1997–1999. Department of Health, Welsh Office, Scottish Office Department of Health, Department of Health and Social Services, Northern Ireland, London
O’Hagan S., Dunn W.B., Brown M., Knowles J.D. and Kell D.B. (2005) Closed-loop, multiobjective optimisation of analytical instrumentation: gas-chromatography-time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. Anal Chem 77, 290–303
Oliver S.G., Winson M.K., Kell D.B. and Baganz F. (1998) Systematic functional analysis of the yeast genome. Trends Biotechnol 16, 373–378
Petricoin E.F., Ardekani A.M., Hitt B.A., Levine P.J., Fusaro V.A., Steinberg S.M., Mills G.B., Simone C., Fishman D.A., Kohn E.C. and Liotta L.A. (2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572–577
Pijnenborg R., Anthony J., Davey D.A., Rees A., Tiltman A., Vercruysse L. and van Assche A. (1991) Placental bed spiral arteries in the hypertensive disorders of pregnancy. Br J Obstet Gynaecol 98, 648–655
Raamsdonk L.M., Teusink B., Broadhurst D., Zhang N., Hayes A., Walsh M.C., Berden J.A., Brindle K.M., Kell D.B., Rowland J.J., Westerhoff H.V., van Dam K. and Oliver S.G. (2001) A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 19, 45–50
Rashed M.S. (2001) Clinical applications of tandem mass spectrometry: ten years of diagnosis and screening for inherited metabolic diseases. J Chromatogr B Biomed Sci Appl 758, 27–48
Roberts J.M., Taylor R.N., Musci T.J., Rodgers G.M., Hubel C.A. and McLaughlin M.K. (1989) Preeclampsia: an endothelial cell disorder. Am J Obstet Gynecol 161, 1200–1204
Rodgers G.M., Taylor R.N. and Roberts J.M. (1988) Preeclampsia is associated with a serum factor cytotoxic to human endothelial cells. Am J Obstet Gynecol 159, 908–914
Shi Y., Evans J.E. and Rock K.L. (2003) Molecular identification of a danger signal that alerts the immune system to dying cells. Nature 425, 516–521
Urbanczyk-Wochniak E., Luedemann A., Kopka J., Selbig J., Roessner-Tunali U., Willmitzer L. and Fernie A.R. (2003) Parallel analysis of transcript and metabolic profiles: a new approach in systems biology. EMBO Rep 4, 989–993
Whitfield P.D., German A.J. and Noble P.J. (2004) Metabolomics: an emerging post-genomic tool for nutrition. Br J Nutr 92, 549–555
Acknowledgments
LK, JM, & PB thank Tommy’s, the Baby Charity, for financial support. DBK thanks the BBSRC, EPSRC and the Royal Society of Chemistry for financial support.
The authors wish to thank Jenny Robinson and Dympna Tansinda for their assistance with the collection of control plasma samples. We thank an anonymous referee for a useful comment.
Author information
Authors and Affiliations
Consortia
Corresponding authors
Rights and permissions
About this article
Cite this article
Kenny, L.C., Dunn, W.B., Ellis, D.I. et al. Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning. Metabolomics 1, 227–234 (2005). https://doi.org/10.1007/s11306-005-0003-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11306-005-0003-1