Sarno, Laura (2019) A metabolomics-based approach for non-invasive screening of fetal central nervous system malformations. [Tesi di dottorato]


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Item Type: Tesi di dottorato
Resource language: English
Title: A metabolomics-based approach for non-invasive screening of fetal central nervous system malformations
Date: 11 December 2019
Number of Pages: 65
Institution: Università degli Studi di Napoli Federico II
Department: Neuroscienze e Scienze Riproduttive ed Odontostomatologiche
Dottorato: Neuroscienze
Ciclo di dottorato: 32
Coordinatore del Corso di dottorato:
Maruotti, Giuseppe MariaUNSPECIFIED
Date: 11 December 2019
Number of Pages: 65
Keywords: central nervous system malformations, metabolomics, prenatal screening
Settori scientifico-disciplinari del MIUR: Area 06 - Scienze mediche > MED/40 - Ginecologia e ostetricia
Date Deposited: 07 Jan 2020 11:00
Last Modified: 17 Nov 2021 12:06

Collection description

State of the art: Central nervous system malformations (CNSM) 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. Metabolomics has great potential as a new approach for the screening of congenital defects because it can be performed on biofluids that can be collected with no risk to the fetus and can potentially detect a wide number of malformations. Objectives: 1. To compare the maternal serum metabolomics profile in cases of fetal CNSM with that one of normal developed fetuses in order to characterize serum metabolomics signature of CNSM. 2. To characterize the maternal serum metabolomics profile of fetal Chromosomal Abnormalities (CA) and fetal Congenital Heart Defects (CHD). 3. To test the accuracy of this metabolomics characterization of congenital anomalies with an independent population. 4. To evaluate if metabolomics profile of CNSM differs from that one of CA and CHD. Material and methods: Metabolomic profiles were obtained from serum of 528 mothers (280 controls, 70 CNSM, 70 CHD and 108 CA), 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: Metabolomics fingerprint of CNSM was defined by increased levels of mayo-inositol,2H3methylbutyric acid, acetic acid, lactic acid, propanoic acid, mannose, gluconic acid and oxalic acid and lower levels of miristica acid, Laurie acid, glucose and stearic acid. The ensemble model was able to correctly identify all cases and controls. We were able to identify differente metabolomics profiles for controls, CNSM, CA and CHD Conclusion: The results of this study are promising, showing a very good accuracy of metabolomics in CNSM detection despite the type of abnormality. This makes our metabolomic approach a viable alternative to currently existing screening systems. Moreover, metabolomics has the ability to identify the enzymatic pathways involved in a pathologic process, giving the possibility to better understand factors related to single disease.


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