Department of Medicine and Surgery and Dentistry, "Scuola Medica Salernitana", University of Salerno, Fisciano, Italy.
Theoreo srl - Spin-off company of the University of Salerno, Via S. De Renzi, 50., Salerno, Italy.
Metabolomics. 2018 May 25;14(6):77. doi: 10.1007/s11306-018-1370-8.
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.
中枢神经系统异常是广泛的先天性出生缺陷，其发病率约为所有出生缺陷的 1%。目前，这些异常是通过超声评估来诊断的。然而，人们强烈需要一种更准确、对操作人员依赖程度更低的筛查方法。
对母体血清进行特征分析，以构建源自中枢神经系统先天畸形的代谢组指纹图谱。
这是一项病例对照的初步研究。采用气相色谱-质谱联用技术对 168 名母亲（98 名对照和 70 名病例）的血清进行代谢组学分析。建立并优化了 9 种机器学习和分类模型。基于个体模型的结果构建了一个集成模型。所有样本均随机分为两组，一组用于训练集，另一组用于诊断性能评估。
集成机器学习模型正确地对所有病例和对照组进行了分类。丙酸、乳酸、葡萄糖酸、苯甲酸、草酸、2-羟基-3-甲基丁酸、乙酸、月桂酸、肉豆蔻酸和硬脂酸、肌醇和甘露糖被选为类别分离中最相关的代谢物。
受胎儿中枢神经系统异常影响的妊娠中期母体血清的代谢组学特征与正常妊娠明显不同。因此，母体血清代谢组学是一种有前途的工具，可以用于此类先天性缺陷的准确和敏感筛查。此外，最相关代谢物及其各自的生化途径的细节可以更好地理解受影响妊娠的整体病理生理学。
