Abstract
Introduction
Chromosomal anomalies (CA) are the most frequent fetal anomalies.
Objective
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 .
Methods
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.
Results
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.
Conclusion
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.
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Acknowledgements
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.
Funding
The research work was funded to MG by UNISA/FARB 2016.
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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.
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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.
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Troisi, J., Sarno, L., Martinelli, P. et al. A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies. Metabolomics 13, 140 (2017). https://doi.org/10.1007/s11306-017-1274-z
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DOI: https://doi.org/10.1007/s11306-017-1274-z