Ex vivo proton spectroscopy (1H‐NMR) analysis of inborn errors of metabolism: Automatic and computer‐assisted analyses. (16th November 2022)

• Record Type: Journal Article
• Authors: Cannet, Claire; Frauendienst‐Egger, Georg; Freisinger, Peter; Götz, Hermann; Götz, Madalina; Himmelreich, Nastassja; Kock, Vanessa; Spraul, Manfred; Bus, Christine; Biskup, Saskia; Trefz, Friedrich
• Other Names: Bathen Tone F. guestEditor.; Cheng Leo L. guestEditor.
• Is Part Of: NMR in biomedicine. Volume 36:Number 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-16
• Subjects: 1H‐NMR -- computer‐assisted analyses -- IEM -- inborn errors of metabolism -- knowledge base -- metabolic databases -- metabolome -- phenylketonuria; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4853
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract: There are about 1500 genetic metabolic diseases. A small number of treatable diseases are diagnosed by newborn screening programs, which are continually being developed. However, most diseases can only be diagnosed based on clinical symptoms or metabolic findings. The main biological fluids used are urine, plasma and, in special situations, cerebrospinal fluid. In contrast to commonly used methods such as gas chromatography and high performance liquid chromatography mass spectrometry, ex vivo proton spectroscopy ( 1 H‐NMR) is not yet used in routine clinical practice, although it has been recommended for more than 30 years. Automatic analysis and improved NMR technology have also expanded the applications used for the diagnosis of inborn errors of metabolism. We provide a mini‐overview of typical applications, especially in urine but also in plasma, used to diagnose common but also rare genetic metabolic diseases with 1 H‐NMR. The use of computer‐assisted diagnostic suggestions can facilitate interpretation of the profiles. In a proof of principle, to date, 182 reports of 59 different diseases and 500 reports of healthy children are stored. The percentage of correct automatic diagnoses was 74%. Using the same 1 H‐NMR profile‐targeted analysis, it is possible to apply an untargeted approach that distinguishes profile differences from healthy individuals. Thus, additional conditions such as lysosomal storage diseases or drug interferences are detectable. Furthermore, because 1 H‐NMR is highly reproducible and can detect a variety of different substance categories, the metabolomic approach is suitable for monitoring patient treatment and revealing additional factors such as nutrition and microbiome metabolism. Besides the progress in analytical techniques, a multiomics approach is most effective to combine metabolomics with, for example, whole exome sequencing, to also diagnose patients with nondetectable metabolic abnormalities in biological fluids. In this mini review we also provide our own data to demonstrate the role of NMR in a multiomics platform in the field of inborn errors of metabolism. Abstract : From the urinary spectrum, 150 metabolites are quantified fully automatically but hundreds of additional metabolites can be identified manually with computer‐assisted proposals for a final diagnosis. A multidisciplinary team decides on further genetic analysis. 1 H‐NMR ex vivo measurement is a robust supplementary method in the investigation of IEM.