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

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

In: ORPHANET J RARE DIS, Vol. 16, No. 1, 441, 20.10.2021.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Saffari, A, Cannet, C, Blaschek, A, Hahn, A, Hoffmann, GF, Johannsen, J, Kirsten, R, Kockaya, M, Kölker, S, Müller-Felber, W, Roos, A, Schäfer, H, Schara, U, Spraul, M, Trefz, FK, Vill, K, Wick, W, Weiler, M, Okun, JG & Ziegler, A 2021, '1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy', ORPHANET J RARE DIS, vol. 16, no. 1, 441. https://doi.org/10.1186/s13023-021-02075-x

APA

Saffari, A., Cannet, C., Blaschek, A., Hahn, A., Hoffmann, G. F., Johannsen, J., Kirsten, R., Kockaya, M., Kölker, S., Müller-Felber, W., Roos, A., Schäfer, H., Schara, U., Spraul, M., Trefz, F. K., Vill, K., Wick, W., Weiler, M., Okun, J. G., & Ziegler, A. (2021). 1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy. ORPHANET J RARE DIS, 16(1), [441]. https://doi.org/10.1186/s13023-021-02075-x

Vancouver

Saffari A, Cannet C, Blaschek A, Hahn A, Hoffmann GF, Johannsen J et al. 1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy. ORPHANET J RARE DIS. 2021 Oct 20;16(1). 441. https://doi.org/10.1186/s13023-021-02075-x

Bibtex

@article{74cd59df133b439d9c604268bdaeada1,
title = "1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy",
abstract = "BACKGROUND: 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 1H-nuclear magnetic resonance (NMR)-based metabolic profiling in identifying non-invasive diagnostic and prognostic urinary fingerprints in children and adolescents with SMA.RESULTS: Urine samples were collected from 29 treatment-na{\"i}ve 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.CONCLUSIONS: This study provides preliminary evidence for the use of 1H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy.",
author = "Afshin Saffari and Claire Cannet and Astrid Blaschek and Andreas Hahn and Hoffmann, {Georg F} and Jessika Johannsen and Romy Kirsten and Musa Kockaya and Stefan K{\"o}lker and Wolfgang M{\"u}ller-Felber and Andreas Roos and Hartmut Sch{\"a}fer and Ulrike Schara and Manfred Spraul and Trefz, {Friedrich K} and Katharina Vill and Wolfgang Wick and Markus Weiler and Okun, {J{\"u}rgen G} and Andreas Ziegler",
note = "{\textcopyright} 2021. The Author(s).",
year = "2021",
month = oct,
day = "20",
doi = "10.1186/s13023-021-02075-x",
language = "English",
volume = "16",
journal = "ORPHANET J RARE DIS",
issn = "1750-1172",
publisher = "BioMed Central Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - 1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy

AU - Saffari, Afshin

AU - Cannet, Claire

AU - Blaschek, Astrid

AU - Hahn, Andreas

AU - Hoffmann, Georg F

AU - Johannsen, Jessika

AU - Kirsten, Romy

AU - Kockaya, Musa

AU - Kölker, Stefan

AU - Müller-Felber, Wolfgang

AU - Roos, Andreas

AU - Schäfer, Hartmut

AU - Schara, Ulrike

AU - Spraul, Manfred

AU - Trefz, Friedrich K

AU - Vill, Katharina

AU - Wick, Wolfgang

AU - Weiler, Markus

AU - Okun, Jürgen G

AU - Ziegler, Andreas

N1 - © 2021. The Author(s).

PY - 2021/10/20

Y1 - 2021/10/20

N2 - BACKGROUND: 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 1H-nuclear magnetic resonance (NMR)-based metabolic profiling in identifying non-invasive diagnostic and prognostic urinary fingerprints in children and adolescents with SMA.RESULTS: 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.CONCLUSIONS: This study provides preliminary evidence for the use of 1H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy.

AB - BACKGROUND: 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 1H-nuclear magnetic resonance (NMR)-based metabolic profiling in identifying non-invasive diagnostic and prognostic urinary fingerprints in children and adolescents with SMA.RESULTS: 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.CONCLUSIONS: This study provides preliminary evidence for the use of 1H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy.

U2 - 10.1186/s13023-021-02075-x

DO - 10.1186/s13023-021-02075-x

M3 - SCORING: Journal article

C2 - 34670613

VL - 16

JO - ORPHANET J RARE DIS

JF - ORPHANET J RARE DIS

SN - 1750-1172

IS - 1

M1 - 441

ER -