Tebani Abdellah, Abily-Donval Lenaig, Afonso Carlos, Marret Stéphane, Bekri Soumeya
Department of Metabolic Biochemistry, Rouen University Hospital, Rouen 76031, France.
Normandie Univ, UNIROUEN, INSERM, CHU Rouen, IRIB, Laboratoire NeoVasc ERI28, Rouen 76000, France.
Int J Mol Sci. 2016 Jul 20;17(7):1167. doi: 10.3390/ijms17071167.
Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of "omic" technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent "omic" technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era.
先天性代谢缺陷（IEM）是一组约500种罕见的遗传疾病，总体估计发病率为1/2500。所涉及的代谢途径的多样性解释了建立其诊断的困难。然而，早期诊断对于成功治疗通常是必不可少的。鉴于一些先天性代谢缺陷之间存在相当大的临床重叠，生化和分子检测对于做出诊断至关重要。传统的生物学诊断程序基于一系列耗时的顺序和分段生化检测。“组学”技术的兴起提供了在不同水平构建生物系统的基本分子的整体视图。代谢组学是基于代谢物的生化特征及其与遗传和环境因素相关变化的最新“组学”技术。本综述阐述了代谢组学技术的基本原理，这些原理使它们能够全面评估个体生化特征及其在精准医学时代IEM研究中的应用报道。
