Jacopo Troisi1,2, Annamaria Landolfi3, Laura Sarno4, Sean Richards5,6, Steven Symes7,6, David Adair6, Carla Ciccone8, Giovanni Scala9, Pasquale Martinelli4, Maurizio Guida3,9. 1. Department of Medicine and Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Fisciano, Italy. troisi@theoreosrl.com. 2. Theoreo srl - Spin-off company of the University of Salerno, Via S. De Renzi, 50., Salerno, Italy. troisi@theoreosrl.com. 3. Department of Medicine and Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Fisciano, Italy. 4. Department of Neurosciences and Reproductive and Dentistry Sciences, University of Naples Federico II, Naples, Italy. 5. Department of Biology, Geology and Environmental Sciences, University of Tennessee at Chattanooga, 615 McCallie Ave., Chattanooga, TN, 37403, USA. 6. Department of Obstetrics and Gynecology, University of Tennessee College of Medicine, Chattanooga, TN, USA. 7. Department of Chemistry and Physics, University of Tennessee at Chattanooga, 615 McCallie Ave., Chattanooga, TN, 37403, USA. 8. "G. Moscati" Hospital, Avellino, Italy. 9. Theoreo srl - Spin-off company of the University of Salerno, Via S. De Renzi, 50., Salerno, Italy.
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.
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.
Entities:
Keywords:
Central nervous system abnormalities; Gas chromatography mass spectrometry; Machine learning; Metabolomics; Screening test
Authors: Gonçalo Graça; Iola F Duarte; António S Barros; Brian J Goodfellow; Sílvia Diaz; Isabel M Carreira; Ana Bela Couceiro; Eulália Galhano; Ana M Gil Journal: J Proteome Res Date: 2009-08 Impact factor: 4.466
Authors: Sílvia O Diaz; António S Barros; Brian J Goodfellow; Iola F Duarte; Eulália Galhano; Cristina Pita; Maria do Céu Almeida; Isabel M Carreira; Ana M Gil Journal: J Proteome Res Date: 2013-05-09 Impact factor: 4.466
Authors: Robert A van den Berg; Huub C J Hoefsloot; Johan A Westerhuis; Age K Smilde; Mariët J van der Werf Journal: BMC Genomics Date: 2006-06-08 Impact factor: 3.969
Authors: Jacopo Troisi; Maria Tafuro; Martina Lombardi; Giovanni Scala; Sean M Richards; Steven J K Symes; Paolo Antonio Ascierto; Paolo Delrio; Fabiana Tatangelo; Carlo Buonerba; Biancamaria Pierri; Pellegrino Cerino Journal: Metabolites Date: 2022-01-25
Authors: Jacopo Troisi; Antonio Mollo; Martina Lombardi; Giovanni Scala; Sean M Richards; Steven J K Symes; Antonio Travaglino; Daniele Neola; Umberto de Laurentiis; Luigi Insabato; Attilio Di Spiezio Sardo; Antonio Raffone; Maurizio Guida Journal: Biomolecules Date: 2022-09-02
Authors: Jacopo Troisi; Antonio Raffone; Antonio Travaglino; Gaetano Belli; Carmen Belli; Santosh Anand; Luigi Giugliano; Pierpaolo Cavallo; Giovanni Scala; Steven Symes; Sean Richards; David Adair; Alessio Fasano; Vincenzo Bottigliero; Maurizio Guida Journal: JAMA Netw Open Date: 2020-09-01