, 13:140 | Cite as

A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies

  • Jacopo Troisi
  • Laura Sarno
  • Pasquale Martinelli
  • Costantino Di Carlo
  • Annamaria Landolfi
  • Giovanni Scala
  • Maurizio Rinaldi
  • Pietro D’Alessandro
  • Carla Ciccone
  • Maurizio Guida
Original Article



Chromosomal anomalies (CA) are the most frequent fetal anomalies.


To evaluate the diagnostic performance of a machine learning ensemble model based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester .


This is a case-control pilot study. Metabolomic profiles have been obtained on serum of 328 mothers (220 controls and 108 cases), using gas chromatography coupled to mass spectrometry. Eight machines learning and classification models were built and optimized. An ensemble model was built using a voting scheme. All samples were randomly divided into two sets. One was used as training set, the other one for diagnostic performance assessment.


Ensemble machine learning model correctly classified all cases and controls. The accuracy was the same for trisomy 21 and 18; also, the other CA were correctly detected. Elaidic, stearic, linolenic, myristic, benzoic, citric and glyceric acid, mannose, 2-hydroxy butyrate, phenylalanine, proline, alanine and 3-methyl histidine were selected as the most relevant metabolites in class separation.


The proposed model, based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester, correctly identifies all the cases of chromosomal abnormalities. Overall, this preliminary analysis appeared suggestive of a metabolic environment conductive to increased oxidative stress and a disturbance in the fetal central nervous system development. Maternal serum metabolomics can be a promising tool in the screening of chromosomal defects. Moreover, metabolomics allows to extend our knowledge about biochemical alterations caused by aneuploidies and responsible for the observed phenotypes.


Chromosomal abnormalities Gas chromatography mass spectrometry Machine learning Metabolomics Screening test 



The authors thank prof. Giuseppe Castaldo from Department of Biochemistry and Medical Biotechnology, University of Naples, Federico II, Naples (Italy), for the kindly intellectual support and for the revision of the manuscript.


The research work was funded to MG by UNISA/FARB 2016.

Compliance with ethical standards

Conflict of interest

J. Troisi, G. Scala, and M. Guida have got an Italian patent for the diagnostic test described in the manuscript (Patent No. 0001423755/2016) and have applied for a PCT extension. All the other authors have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was approved by the ethics committee CEI OORR San Giovanni di Dio e Ruggi D’Aragona (IRB No. 4/2013) and a written consent form was signed by each participant.

Supplementary material

11306_2017_1274_MOESM1_ESM.docx (292 kb)
Supplementary material 1 (DOCX 291 KB)


  1. Abbott, D. W. (1999). Combining Models to Improve Classifier Accuracy and Robustness. Paper presented at the 2nd International Conference on Information Fusion, San Jose, CA.Google Scholar
  2. Akolekar, R., Beta, J., Picciarelli, G., Ogilvie, C., & D’Antonio, F. (2015). Procedure-related risk of miscarriage following amniocentesis and chorionic villus sampling: A systematic review and meta-analysis. Ultrasound in Obstetrics & Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology, 45, 16–26. doi: 10.1002/uog.14636.CrossRefGoogle Scholar
  3. Allen, E. G., et al. (2009). Maternal age and risk for trisomy 21 assessed by the origin of chromosome nondisjunction: A report from the Atlanta and National Down Syndrome Projects. Human Genetics, 125, 41–52. doi: 10.1007/s00439-008-0603-8.CrossRefPubMedGoogle Scholar
  4. Amorini, A. M., et al. (2012). Metabolic profile of amniotic fluid as a biochemical tool to screen for inborn errors of metabolism and fetal anomalies. Molecular and Cellular Biochemistry, 359, 205–216. doi: 10.1007/s11010-011-1015-y.CrossRefPubMedGoogle Scholar
  5. Bahado-Singh, R. O., et al. (2012). Metabolomics and first-trimester prediction of early-onset preeclampsia. The Journal of Maternal-fetal & Neonatal Medicine: The Official Journal of the European Association of Perinatal Medicine, The Federation of Asia and Oceania Perinatal Societies, The International Society of Perinatal Obstetricians, 25, 1840–1847. doi: 10.3109/14767058.2012.680254.CrossRefGoogle Scholar
  6. Bahado-Singh, R. O., et al. (2013a). Metabolomic analysis for first-trimester trisomy 18 detection. American Journal of Obstetrics and Gynecology, 209, 65e1–9. doi: 10.1016/j.ajog.2013.03.028.CrossRefGoogle Scholar
  7. Bahado-Singh, R. O., et al. (2013b). Metabolomic analysis for first-trimester Down syndrome prediction. American Journal of Obstetrics and Gynecology, 208, 371.e1–8. doi: 10.1016/j.ajog.2012.12.035.CrossRefGoogle Scholar
  8. Bahado-Singh, R. O., et al. (2014). Metabolomic prediction of fetal congenital heart defect in the first trimester. American Journal of Obstetrics and Gynecology, 211, 240.e1–240.e14 doi: 10.1016/j.ajog.2014.03.056.CrossRefGoogle Scholar
  9. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: WadsworthGoogle Scholar
  10. Busciglio, J., & Yankner, B. A. (1995). Apoptosis and increased generation of reactive oxygen species in Down’s syndrome neurons in vitro. Nature, 378, 776–779. doi: 10.1038/378776a0.CrossRefPubMedGoogle Scholar
  11. Charkiewicz, K., Blachnio-Zabielska, A., Zbucka-Kretowska, M., Wolczynski, S., & Laudanski, P. (2015). Maternal plasma and amniotic fluid sphingolipids profiling in fetal Down syndrome. PLoS One, 10, e0127732. doi: 10.1371/journal.pone.0127732.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297. doi: 10.1007/bf00994018.Google Scholar
  13. Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21–27. doi: 10.1109/TIT.1967.1053964.CrossRefGoogle Scholar
  14. Demirhan, O., Tastemir, D., Günesacar, R., Güzel, A., & Alptekin, D. (2011). The first report described as an important study: The association of mannose-binding lectin gene 2 polymorphisms in children with down syndrome. Indian Journal of Human Genetics, 17, 59–64. doi: 10.4103/0971-6866.86176.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Diaz, S. O., et al. (2011). Metabolic biomarkers of prenatal disorders: An exploratory NMR metabonomics study of second trimester maternal urine and blood plasma. Journal of Proteome Research, 10, 3732–3742. doi: 10.1021/pr200352m.CrossRefPubMedGoogle Scholar
  16. Diaz, S. O., et al. (2013). Second trimester maternal urine for the diagnosis of trisomy 21 and prediction of poor pregnancy outcomes. Journal of Proteome Research, 12, 2946–2957. doi: 10.1021/pr4002355.CrossRefPubMedGoogle Scholar
  17. Dietterich, T. G. (2000) Ensemble methods in machine learning. In International workshop on multiple classifier systems (pp. 1–15). Berlin: Springer.Google Scholar
  18. Domingos, P. (1999). Metacost: A general method for making classifiers cost-sensitive. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, p 155–164.Google Scholar
  19. Elrahman, S. M. A., & Abraham, A. (2013). A review of class imbalance problem. Journal of Network and Innovative Computing, 1, 332–340.Google Scholar
  20. Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179–188. doi: 10.1111/j.1469-1809.1936.tb02137.x.CrossRefGoogle Scholar
  21. Forabosco, A., Percesepe, A., & Santucci, S. (2009). Incidence of non-age-dependent chromosomal abnormalities: A population-based study on 88965 amniocenteses. European Journal of Human Genetics, 17, 897–903. doi: 10.1038/ejhg.2008.265.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Gil, M. M., Accurti, V., Santacruz, B., & Plana, M. N. (2017). Analysis of cell-free DNA in maternal blood in screening for aneuploidies: Updated meta-analysis. Ultrasound in Obstetrics & Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology. doi: 10.1002/uog.17484.Google Scholar
  23. Gil, M. M., Quezada, M. S., Revello, R., Akolekar, R., & Nicolaides, K. H. (2015). Analysis of cell-free DNA in maternal blood in screening for fetal aneuploidies: Updated meta-analysis. Ultrasound in Obstetrics & Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology, 45, 249–266. doi: 10.1002/uog.14791.CrossRefGoogle Scholar
  24. Hand, D. J., & Yu, K. (2001). Idiot’s Bayes: Not so stupid after all? International Statistical Review/Revue Internationale de Statistique, 69, 385–398. doi: 10.2307/1403452.Google Scholar
  25. Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research Electronic Data Capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42, 377–381. doi: 10.1016/j.jbi.2008.08.010.CrossRefPubMedGoogle Scholar
  26. Ho, T. K. (1995) Random decision forests. In Document Analysis and Recognition, 1995, Proceedings of the Third International Conference on (Vol. 1, p 278–282). IEEE.Google Scholar
  27. Ichinohe, A., Kanaumi, T., Takashima, S., Enokido, Y., Nagai, Y., & Kimura, H. (2005). Cystathionine beta-synthase is enriched in the brains of Down’s patients. Biochemical and Biophysical Research Communications, 338, 1547–1550. doi: 10.1016/j.bbrc.2005.10.118.CrossRefPubMedGoogle Scholar
  28. Johnson, R. C., McKean, C. M., & Shah, S. N. (1977). Fatty acid composition of lipids in cerebral myelin and synaptosomes in phenylketonuria and Down syndrome. Archives of Neurology, 34, 288–294.Google Scholar
  29. Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2014). Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Research, 42, D199–D205. doi: 10.1093/nar/gkt1076.CrossRefPubMedGoogle Scholar
  30. Karnovsky, A., et al. (2012). Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics (Oxford, England), 28, 373–380. doi: 10.1093/bioinformatics/btr661.CrossRefGoogle Scholar
  31. Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., & Pfister, H. (2014). UpSet: Visualization of Intersecting Sets. IEEE Transactions on Visualization and Computer Graphics, 20, 1983–1992. doi: 10.1109/TVCG.2014.2346248.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Ma, H., et al. (2007). The Edinburgh human metabolic network reconstruction and its functional analysis. Molecular Systems Biology, 3, 135–135. doi: 10.1038/msb4100177.CrossRefPubMedPubMedCentralGoogle Scholar
  33. Murphy, E. J., Schapiro, M. B., Rapoport, S. I., & Shetty, H. U. (2000). Phospholipid composition and levels are altered in Down syndrome brain. Brain Research, 867, 9–18.CrossRefPubMedGoogle Scholar
  34. Nicolaides, K. H. (2011). Screening for fetal aneuploidies at 11 to 13 weeks. Prenatal Diagnosis, 31, 7–15. doi: 10.1002/pd.2637.CrossRefPubMedGoogle Scholar
  35. Nishida, K., Ono, K., Kanaya, S., & Takahashi, K. (2014). KEGGscape: A Cytoscape app for pathway data integration. F1000Research, 3, 144. doi: 10.12688/f1000research.4524.1.PubMedPubMedCentralGoogle Scholar
  36. Pinto, J., et al. (2014). Maternal plasma phospholipids are altered in trisomy 21 cases and prior to preeclampsia and preterm outcomes. Rapid Communications in Mass Spectrometry, 28, 1635–1638. doi: 10.1002/rcm.6941.CrossRefPubMedGoogle Scholar
  37. Pinto, J., et al. (2015). Impact of fetal chromosomal disorders on maternal blood metabolome: Toward new biomarkers?. American Journal of Obstetrics and Gynecology, 213, 841.e1–841.e15. doi: 10.1016/j.ajog.2015.07.032.CrossRefGoogle Scholar
  38. R Development Core Team. (2012). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
  39. Roobaert, D., Karakoulas, G., & Chawla, N. V. (2006). Information gain, correlation and support vector machines. In I. Guyon, M. Nikravesh, S. Gunn & L. A. Zadeh (Eds.), Feature extraction: Foundations and applications.(pp. 463-470). Berlin: Springer.Google Scholar
  40. Salek, R. M., Steinbeck, C., Viant, M. R., Goodacre, R., & Dunn, W. B. (2013). The role of reporting standards for metabolite annotation and identification in metabolomic studies. GigaScience, 2, 13. doi: 10.1186/2047-217X-2-13.CrossRefPubMedPubMedCentralGoogle Scholar
  41. Santorum, M., Wright, D., Syngelaki, A., Karagioti, N., & Nicolaides, K. H. (2016). Accuracy of first trimester combined test in screening for trisomies 21, 18 and 13. Ultrasound in Obstetrics & Gynecology: The Official Journal of The International Society of Ultrasound in Obstetrics and Gynecology, 49, 714–720. doi: 10.1002/uog.17283.CrossRefGoogle Scholar
  42. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117 doi: 10.1016/j.neunet.2014.09.003.CrossRefPubMedGoogle Scholar
  43. Trivedi, D. K., & Iles, R. K. (2015). Shotgun metabolomic profiles in maternal urine identify potential mass spectral markers of abnormal fetal biochemistry—Dihydrouracil and progesterone in the metabolism of Down syndrome. Biomedical Chromatography: BMC, 29, 1173–1183. doi: 10.1002/bmc.3404.CrossRefPubMedGoogle Scholar
  44. van den Berg, R. A., Hoefsloot, H. C. J., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC Genomics, 7, 142–142. doi: 10.1186/1471-2164-7-142.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Weiss, G. M., & Provost, F. (2003). Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research, 19, 315–354.Google Scholar
  46. Wilk, T., & Wozniak, M. (2011). Complexity and multithreaded implementation analysis of one class-classifiers fuzzy combiner. Paper presented at the Proceedings of the 6th international conference on Hybrid artificial intelligent systems—Volume Part II, Wroclaw, Poland.Google Scholar
  47. Wold, S., Johansson, E., & Cocchi, M. (1993). PLS-partial least squares projections to latent structures. 3D QSAR in Drug Design, 1, 523–550.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Jacopo Troisi
    • 1
    • 2
  • Laura Sarno
    • 3
  • Pasquale Martinelli
    • 3
  • Costantino Di Carlo
    • 3
  • Annamaria Landolfi
    • 1
  • Giovanni Scala
    • 2
  • Maurizio Rinaldi
    • 1
  • Pietro D’Alessandro
    • 1
  • Carla Ciccone
    • 4
  • Maurizio Guida
    • 1
    • 2
  1. 1.Department of Medicine and Surgery and Dentistry, “Scuola Medica Salernitana”University of SalernoFiscianoItaly
  2. 2.Theoreo srl – Spin-off company of the University of SalernoSalernoItaly
  3. 3.Department of Neurosciences and Reproductive and Dentistry SciencesUniversity of Naples Federico IINaplesItaly
  4. 4.“G. Moscati” HospitalAvellinoItaly

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