Metabolomics: A challenge for detecting and monitoring inborn errors of metabolism.,Il ruolo della metabolomica nella diagnosi e nel monitoraggio delle malattie metaboliche ereditarie

Timely newborn screening and genetic profile are crucial in early recognition and treatment of inborn errors of metabolism (IEMs). A proposed nosology of IEMs has inserted 1015 well-characterized IEMs causing alterations in specific metabolic pathways. With the increasing expansion of metabolomics in the clinical biochemistry and laboratory medicine communities, several research groups have focused their interest the analysis of metabolites and their interconnections in IEMs. Metabolomics has the property to extend metabolic information leading to achieve an accurate diagnosis for an individual patient and to discover novel IEMs. Structural and functional information on 247 metabolites associated with 147 IEMs and 202 metabolic pathways involved in various IEMs have been reported in the human metabolome data base (HMDB). For each metabolic gene, a new computational approach can be developed for predicting a set of metabolites that are expected to change their concentration in urine, blood and other biological fluids after gene knockout. Both the targeted and the untargeted mass spectrometry (MS)-based metabolomic approaches have been used to expand the range of disease-associate metabolites. The quantitative targeted approach, in conjunction with chemometrics, can be considered a basic tool for validating known diagnostic biomarkers in various metabolic disorders. The untargeted approach increases the identification of new biomarkers in known IEMs and allows pathways analysis. Urine is an ideal biological fluid for metabolomics in neonatology; however, the lack of standardization of the preanalytical phase may originate potential interferences in metabolomic studies. Integrating genomic with metabolomic data represents the current challenge for improving diagnosis and prognosis of IEMs. The goals consist of the identification of both metabolically active loci and genes relevant to a disease phenotype, that means deriving disease-specific biological insights.