1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy

• Published in: Orphanet journal of rare diseases 2021-10, Vol.16 (1), p.1-441, Article 441
• Main Authors: Saffari, Afshin, Cannet, Claire, Blaschek, Astrid, Hahn, Andreas, Hoffmann, Georg F, Johannsen, Jessika, Kirsten, Romy, Kockaya, Musa, Kölker, Stefan, Müller-Felber, Wolfgang, Roos, Andreas, Schäfer, Hartmut, Schara, Ulrike, Spraul, Manfred, Trefz, Friedrich K, Vill, Katharina, Wick, Wolfgang, Weiler, Markus, Okun, Jürgen G, Ziegler, Andreas
• Format: Article
• Language: English
• Subjects: 1H-nuclear magnetic resonance; Age; Analysis; Biomarkers; Children; Creatinine; Diagnosis; Diagnosis, Noninvasive; Disease; Duchenne's muscular dystrophy; Feasibility studies; Gender; Learning algorithms; Medical diagnosis; Medical research; Medical screening; Metabolic profiling; Metabolism; Metabolites; Metabolomics; Methods; Mutation; Neonates; Neuromuscular diseases; NMR; Nuclear magnetic resonance; Patients; Prediction models; Rare diseases; SMA; Spectrum analysis; Spinal muscular atrophy; Urinary fingerprints; Urine
• ISSN: 1750-1172; 1750-1172
• DOI: 10.1186/s13023-021-02075-x

5q spinal muscular atrophy (SMA) is a disabling and life-limiting neuromuscular disease. In recent years, novel therapies have shown to improve clinical outcomes. Yet, the absence of reliable biomarkers renders clinical assessment and prognosis of possibly already affected newborns with a positive newborn screening result for SMA imprecise and difficult. Therapeutic decisions and stratification of individualized therapies remain challenging, especially in symptomatic children. The aim of this proof-of-concept and feasibility study was to explore the value of .sup.1H-nuclear magnetic resonance (NMR)-based metabolic profiling in identifying non-invasive diagnostic and prognostic urinary fingerprints in children and adolescents with SMA. Urine samples were collected from 29 treatment-naïve SMA patients (5 pre-symptomatic, 9 SMA 1, 8 SMA 2, 7 SMA 3), 18 patients with Duchenne muscular dystrophy (DMD) and 444 healthy controls. Using machine-learning algorithms, we propose a set of prediction models built on urinary fingerprints that showed potential diagnostic value in discriminating SMA patients from controls and DMD, as well as predictive properties in separating between SMA types, allowing predictions about phenotypic severity. Interestingly, preliminary results of the prediction models suggest additional value in determining biochemical onset of disease in pre-symptomatic infants with SMA identified by genetic newborn screening and furthermore as potential therapeutic monitoring tool. This study provides preliminary evidence for the use of .sup.1H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy.