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Identification of biochemical determinants for diagnosis and prediction of severity in 5q spinal muscular atrophy using 1H-nuclear magnetic resonance metabolic profiling in patient-derived biofluids

This study explores the potential of 1H-NMR spectroscopy-based metabolic profiling in various biofluids as a diagnostic and predictive modality to assess disease severity in individuals with 5q spinal muscular atrophy. A total of 213 biosamples (urine, plasma, and CSF) from 153 treatment-naïve pati...

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Main Authors: Saffari, Afshin (Author) , Niesert, Moritz (Author) , Cannet, Claire (Author) , Blaschek, Astrid (Author) , Hahn, Andreas (Author) , Johannsen, Jessika (Author) , Kockaya, Musa (Author) , Kölbel, Heike (Author) , Hoffmann, Georg F. (Author) , Claus, Peter (Author) , Kölker, Stefan (Author) , Müller-Felber, Wolfgang (Author) , Roos, Andreas (Author) , Schara-Schmidt, Ulrike (Author) , Trefz, Friedrich K. (Author) , Vill, Katharina (Author) , Wick, Wolfgang (Author) , Weiler, Markus (Author) , Okun, Jürgen G. (Author) , Ziegler, Andreas (Author)
Format: Article (Journal)
Language:English
Published: 12 November 2024
In: International journal of molecular sciences
Year: 2024, Volume: 25, Issue: 22, Pages: 1-19
ISSN:1422-0067
DOI:10.3390/ijms252212123
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.3390/ijms252212123
Verlag, kostenfrei, Volltext: https://www.mdpi.com/1422-0067/25/22/12123
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Author Notes:Afshin Saffari, Moritz Niesert, Claire Cannet, Astrid Blaschek, Andreas Hahn, Jessika Johannsen, Musa Kockaya, Heike Kölbel, Georg F. Hoffmann, Peter Claus, Stefan Kölker, Wolfgang Müller-Felber, Andreas Roos, Ulrike Schara-Schmidt, Friedrich K. Trefz, Katharina Vill, Wolfgang Wick, Markus Weiler, Jürgen G. Okun and Andreas Ziegler
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Summary:This study explores the potential of 1H-NMR spectroscopy-based metabolic profiling in various biofluids as a diagnostic and predictive modality to assess disease severity in individuals with 5q spinal muscular atrophy. A total of 213 biosamples (urine, plasma, and CSF) from 153 treatment-naïve patients with SMA across five German centers were analyzed using 1H-NMR spectroscopy. Prediction models were developed using machine learning algorithms which enabled the patients with SMA to be grouped according to disease severity. A quantitative enrichment analysis was employed to identify metabolic pathways associated with disease progression. The results demonstrate high sensitivity (84-91%) and specificity (91-94%) in distinguishing treatment-naïve patients with SMA from controls across all biofluids. The urinary and plasma profiles differentiated between early-onset (type I) and later-onset (type II/III) SMA with over 80% accuracy. Key metabolic differences involved alterations in energy and amino acid metabolism. This study suggests that 1H-NMR spectroscopy based metabolic profiling may be a promising, non-invasive tool to identify patients with SMA and for severity stratification, potentially complementing current diagnostic and prognostic strategies in SMA management.
Item Description:Die Ziffer 1 ist im Titel hochgestellt
Gesehen am 20.05.2025
Physical Description:Online Resource
ISSN:1422-0067
DOI:10.3390/ijms252212123

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