Abstract
Background
Central nervous system anomalies represent a wide range of congenital birth defects, with an incidence of approximately 1% of all births. They are currently diagnosed using ultrasound evaluation. However, there is strong need for a more accurate and less operator-dependent screening method.
Objectives
To perform a characterization of maternal serum in order to build a metabolomic fingerprint resulting from congenital anomalies of the central nervous system.
Methods
This is a case–control pilot study. Metabolomic profiles were obtained from serum of 168 mothers (98 controls and 70 cases), using gas chromatography coupled to mass spectrometry. Nine machine learning and classification models were built and optimized. An ensemble model was built based on results from the individual models. All samples were randomly divided into two groups. One was used as training set, the other one for diagnostic performance assessment.
Results
Ensemble machine learning model correctly classified all cases and controls. Propanoic, lactic, gluconic, benzoic, oxalic, 2-hydroxy-3-methylbutyric, acetic, lauric, myristic and stearic acid and myo-inositol and mannose were selected as the most relevant metabolites in class separation.
Conclusion
The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal central nervous system anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is therefore a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the details of the most relevant metabolites and their respective biochemical pathways allow better understanding of the overall pathophysiology of affected pregnancies.
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References
Akarachantachote, N., Chadcham, S., & Saithanu, K. (2014). Cutoff threshold of variable importance in projection for variable selection. International Journal of Pure and Applied Mathematics, 94(3), 307–322.
Bahado-Singh, R. O., Akolekar, R., Mandal, R., Dong, E., Xia, J., Kruger, M., et al. (2013). Metabolomic analysis for first-trimester Down syndrome prediction. American Journal of Obstetrics and Gynecology, 208(5), 371.e1–371.e8. https://doi.org/10.1016/j.ajog.2012.12.035.
Bano, S. (2012). Congenital malformation of the brain. In V. Chaudhary & P. Bright (Eds.), Neuroimaging (Ch. 1). Rijeka: InTech. https://doi.org/10.5772/22936.
Bonab, H. R., & Can, F. (2017). Less is more: A comprehensive framework for the number of components of ensemble classifiers. arXiv:1709.02925.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Boca Raton: CRC Press.
Carroll, S. G., Porter, H., Abdel-Fattah, S., Kyle, P. M., & Soothill, P. W. (2000). Correlation of prenatal ultrasound diagnosis and pathologic findings in fetal brain abnormalities. Ultrasound in Obstetrics & Gynecology, 16(2), 149–153. https://doi.org/10.1046/j.1469-0705.2000.00199.x.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018.
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964.
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics, 44(3), 837–845.
Diaz, S. O., Barros, A. S., Goodfellow, B. J., Duarte, I. F., Galhano, E., Pita, C., et al. (2013). Second trimester maternal urine for the diagnosis of trisomy 21 and prediction of poor pregnancy outcomes. Journal of Proteome Research, 12(6), 2946–2957. https://doi.org/10.1021/pr4002355.
Diaz, S. O., Pinto, J., Graca, G., Duarte, I. F., Barros, A. S., Galhano, E., 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(8), 3732–3742. https://doi.org/10.1021/pr200352m.
Dietterich, T. G. (2000). Ensemble methods in machine learning. In J. Kittler & F. Roli (Eds.), Multiple classifier systems (pp. 1–15). Berlin: Springer.
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 (pp. 155–164). San Diego, CA: ACM.
Elrahman, S. M. A., & Abraham, A. (2013). A review of class imbalance problem. Journal of Network and Innovative Computing, 1(2013), 332–340.
Fisher, R. A. (1936). The use of multiple measurement in taxonomic problems. Annals of Eugenics, 7(2), 179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x.
Freinkel, N., Lewis, N. J., Akazawa, S., Roth, S. I., & Gorman, L. (1984). The honeybee syndrome—Implications of the teratogenicity of mannose in rat-embryo culture. The New England Journal of Medicine, 310(4), 223–230. https://doi.org/10.1056/NEJM198401263100404.
Frey, L., & Hauser, W. A. (2003). Epidemiology of neural tube defects. Epilepsia, 44(s3), 4–13.
Girgis, M. Y., Mansour, L. A., Abdallah, N., Kamel, A. F., & Antar, A. (2010). A Prospective Study on Congenital CNS malformations in Neuro-Pediatric Unit Cairo University. Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 47(2), 275–280.
Graca, G., Duarte, I. F., Barros, A. S., Goodfellow, B. J., Diaz, S., Carreira, I. M., et al. (2009). (1)H NMR based metabonomics of human amniotic fluid for the metabolic characterization of fetus malformations. Journal of Proteome Research, 8(8), 4144–4150. https://doi.org/10.1021/pr900386f.
Greene, D. A., Lattimer, S. A., & Sima, A. A. (1988). Are disturbances of sorbitol, phosphoinositide, and Na+-K+-ATPase regulation involved in pathogenesis of diabetic neuropathy? Diabetes, 37(6), 688–693.
Hand David, J., & Yu, K. (2007). Idiot’s Bayes—Not so stupid after all? International Statistical Review, 69(3), 385–398. https://doi.org/10.1111/j.1751-5823.2001.tb00465.x.
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(2), 377–381. https://doi.org/10.1016/j.jbi.2008.08.010.
Herman-Sucharska, I., Bekiesinska-Figatowska, M., & Urbanik, A. (2009). Fetal central nervous system malformations on MR images. Brain & Development, 31(3), 185–199. https://doi.org/10.1016/j.braindev.2008.07.007.
Ho, T. K. (1995). Random decision forests (Vol. 1, pp. 278–282). Presented at the Document analysis and recognition, 1995. In Proceedings of the third international conference on, IEEE.
Hosmer, D. W. Jr., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). Hoboken: Wiley.
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(Database issue), D199–D205. https://doi.org/10.1093/nar/gkt1076.
Karnovsky, A., Weymouth, T., Hull, T., Tarcea, V. G., Scardoni, G., Laudanna, C., et al. (2012). Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics, 28(3), 373–380. https://doi.org/10.1093/bioinformatics/btr661.
Kinoshita, J. H. (1974). Mechanisms initiating cataract formation. Proctor Lecture. Investigative Ophthalmology, 13(10), 713–724.
Le Cessie, S., & Van Houwelingen, J. C. (1992). Ridge estimators in logistic regression. Journal of the Royal Statistical Society Series C, 41(1), 191–201. https://doi.org/10.2307/2347628.
Ma, H., Sorokin, A., Mazein, A., Selkov, A., Selkov, E., Demin, O., & Goryanin, I. (2007). The Edinburgh human metabolic network reconstruction and its functional analysis. Molecular Systems Biology, 3, 135–135. https://doi.org/10.1038/msb4100177.
Maruotti, G. M., Saccone, G., D’Antonio, F., Berghella, V., Sarno, L., Morlando, M., et al. (2016). Diagnostic accuracy of intracranial translucency in detecting spina bifida: A systematic review and meta-analysis. Prenatal Diagnosis, 36(11), 991–996. https://doi.org/10.1002/pd.4883.
Nishida, K., Ono, K., Kanaya, S., & Takahashi, K. (2014). KEGGscape: A Cytoscape app for pathway data integration. F1000Research, 3, 144. https://doi.org/10.12688/f1000research.4524.1.
Onkar, D., Onkar, P., & Mitra, K. (2014). Evaluation of fetal central nervous system anomalies by ultrasound and its anatomical co-relation. Journal of Clinical and Diagnostic Research: JCDR, 8(6), AC05–AC07. https://doi.org/10.7860/JCDR/2014/8052.4437.
Oshiro, T. M., Perez, P. S., & Baranauskas, J. A. (2012). How many trees in a random forest? In P. Perner (Ed.), Machine learning and data mining in pattern recognition (pp. 154–168). Berlin: Springer.
Pashaj, S., & Merz, E. (2016). Detection of fetal corpus callosum abnormalities by means of 3D ultrasound. Ultraschall in der Medizin (Stuttgart, Germany: 1980), 37(2), 185–194. https://doi.org/10.1055/s-0041-108565.
Paul, L. K., Brown, W. S., Adolphs, R., Tyszka, J. M., Richards, L. J., Mukherjee, P., & Sherr, E. H. (2007). Agenesis of the corpus callosum: Genetic, developmental and functional aspects of connectivity. Nature Reviews Neuroscience, 8(4), 287–299. https://doi.org/10.1038/nrn2107.
Pinto, J., Almeida, L. M., Martins, A. S., Duarte, D., Domingues, M. R. M., Barros, A. S., et al. (2015). Impact of fetal chromosomal disorders on maternal blood metabolome: Toward new biomarkers? American Journal of Obstetrics and Gynecology, 213(6), 841.e1–841.e15. https://doi.org/10.1016/j.ajog.2015.07.032.
RDevelopment, CORE TEAM, R (2008). R: A language and environment for statistical computing. Austria: R Foundation for Statistical Computing Vienna.
Salomon, L. J., & Garel, C. (2007). Magnetic resonance imaging examination of the fetal brain. Ultrasound in Obstetrics & Gynecology, 30(7), 1019–1032. https://doi.org/10.1002/uog.5176.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003.
Shchelochkov, O. A., Carrillo, N., & Venditti, C. (2012). Propionic acidemia. GeneReviews® [Internet].
Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics, 3(3), 211–221. https://doi.org/10.1007/s11306-007-0082-2.
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(8), 1173–1183. https://doi.org/10.1002/bmc.3404.
Troisi, J., Sarno, L., Martinelli, P., Di Carlo, C., Landolfi, A., Scala, G., et al. (2017). A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies. Metabolomics, 13(11), 140. https://doi.org/10.1007/s11306-017-1274-z.
van den Berg, R. A., Hoefsloot, H. C., 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(1), 142. https://doi.org/10.1186/1471-2164-7-142.
Wehrens, R., Bloemberg, T. G., & Eilers, P. H. C. (2015). Fast parametric time warping of peak lists. Bioinformatics, 31(18), 3063–3065. https://doi.org/10.1093/bioinformatics/btv299.
Weiss, G., & Provost, F. (2001). The effect of class distribution on classifier learning: An empirical study.
Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, 4(2), 65–85. https://doi.org/10.1007/BF00175354.
Wilk, T., & Wozniak, M. (2011). Complexity and multithreaded implementation analysis of one class-classifiers fuzzy combiner. In Proceedings of the 6th international conference on Hybrid artificial intelligent systems—Volume Part II (pp. 237–244). Wroclaw: Springer.
Wold, S., Sjöström, M., & Eriksson, L. (1998). Partial least squares projections to latent structures (PLS) in chemistry. Encyclopedia of Computational Chemistry. https://doi.org/10.1002/0470845015.cpa012.
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. PLS Methods, 58(2), 109–130. https://doi.org/10.1016/S0169-7439(01)00155-1.
Wolinsky, H. (2006). The battle of Helsinki: Two troublesome paragraphs in the Declaration of Helsinki are causing a furore over medical research ethics. EMBO Reports, 7(7), 670–672. https://doi.org/10.1038/sj.embor.7400743.
Zheng, X., Su, M., Pei, L., Zhang, T., Ma, X., Qiu, Y., et al. (2011). Metabolic signature of pregnant women with neural tube defects in offspring. Journal of Proteome Research, 10(10), 4845–4854. https://doi.org/10.1021/pr200666d.
Funding
This study was supported by FARBS 2016.
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Authors and Affiliations
Contributions
JT, GS and MG conceived and designed the experimental study, LS, CC, PM and MG enrolled pregnant women and performed the clinical evaluations and the samples’ collection. JT, AL and GS performed metabolome extraction, purification, derivatization and analysis. JT performed statistical analysis and machine learning training and test. JT, LS, SR and SS wrote the manuscript. DA, SR, SS, PM and MG edited the manuscript. All the authors approved the final version of the manuscript.
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Conflict of interest
J. Troisi, G. Scala, and M. Guida have 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 local ethics committee (IRB n.4/2013) and a written consent form was signed by each participant.
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Troisi, J., Landolfi, A., Sarno, L. et al. A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies. Metabolomics 14, 77 (2018). https://doi.org/10.1007/s11306-018-1370-8
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DOI: https://doi.org/10.1007/s11306-018-1370-8
Keywords
- Central nervous system abnormalities
- Gas chromatography mass spectrometry
- Machine learning
- Metabolomics
- Screening test