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A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies



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.


To perform a characterization of maternal serum in order to build a metabolomic fingerprint resulting from congenital anomalies of the central nervous system.


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.


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.


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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This study was supported by FARBS 2016.

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Authors and Affiliations



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.

Corresponding author

Correspondence to Jacopo Troisi.

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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).

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  • Central nervous system abnormalities
  • Gas chromatography mass spectrometry
  • Machine learning
  • Metabolomics
  • Screening test