Dataset Information

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-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.

SUBMITTER: Saffari A

PROVIDER: S-EPMC8527822 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Saffari Afshin A Cannet Claire C Blaschek Astrid A Hahn Andreas A Hoffmann Georg F GF Johannsen Jessika J Kirsten Romy R Kockaya Musa M Kölker Stefan S Müller-Felber Wolfgang W Roos Andreas A Schäfer Hartmut H Schara Ulrike U Spraul Manfred M Trefz Friedrich K FK Vill Katharina K Wick Wolfgang W Weiler Markus M Okun Jürgen G JG Ziegler Andreas A

Orphanet journal of rare diseases 20211020 1

<h4>Background</h4>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. ...[more]

PMID: 34670613

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Project description:BackgroundNusinersen was the first approved disease-modifying therapy for all 5q-spinal muscular atrophy (SMA) patients regardless of age or disease severity. Its efficacy in adults has recently been demonstrated in a large cohort by motor outcome measures, which were only partially suitable to detect changes in very mildly or severely affected patients. Patient-reported outcome measures (PROs) have been suggested as a valuable addition. Here, we aimed to assess treatment satisfaction and investigate whether it may be a useful PRO to monitor SMA patients.MethodsWe enrolled 91 mainly adult 5q-SMA patients treated with nusinersen in a national, multicenter, cross-sectional observational study. 21 patients underwent longitudinal follow up. Patients' satisfaction with treatment in four dimensions (global, effectiveness, convenience, side effects) was assessed by the Treatment Satisfaction Questionnaire for Medication German version 1.4 (TSQM-1.4©) and related to clinical parameters, motor scores, and treatment duration.ResultsMore than 90% of SMA patients were consistently satisfied over a median treatment duration of 10 months. Highest mean scores were observed in the dimensions 'side effects,' 'global satisfaction,' and 'effectiveness' (93.5 ± 14.8 versus 73.1 ± 21.0 and 64.8 ± 20.6, respectively). Patients' satisfaction with the convenience of treatment was considerably lower (43.6 ± 20.2). Interestingly, satisfaction with the effectiveness was higher in ambulatory (p = 0.014) compared with non-ambulatory patients and directly correlated to motor outcome measures. Five non-ambulatory patients withdrew from therapy. All of them presented with a deterioration of motor outcome measures and reported dissatisfaction with treatment effectiveness and convenience.ConclusionMost patients were satisfied with nusinersen treatment effectiveness. Less severely affected patients indicated higher satisfaction. The TSQM-1.4© helped to identify therapy non-responders, who mainly addressed dissatisfaction with effectiveness and convenience. We suggest introducing the TSQM-1.4© as an additional PRO in SMA into clinical practice.

Project description:Background5q-Spinal muscular atrophy (SMA) is now one of the 5% treatable rare diseases worldwide. As disease-modifying therapies alter disease progression and patient phenotypes, paediatricians and consulting disciplines face new unknowns in their treatment decisions. Conclusions made from historical patient data sets are now mostly limited, and new approaches are needed to ensure our continued best standard-of-care practices for this exceptional patient group. Here, we present a data-driven machine learning approach to a rare disease data set to predict spinal muscular atrophy (SMA)-associated scoliosis.MethodsWe collected data from 84 genetically confirmed 5q-SMA patients who have received novel SMA therapies. We performed expert domain knowledge-directed feature engineering, correlation and predictive power score (PPS) analyses for feature selection. To test the predictive performance of the selected features, we trained a Random Forest Classifier and evaluated model performance using standard metrics.ResultsThe SMA data set consisted of 1304 visits and over 360 variables. We performed feature engineering for variables related to 'interventions', 'devices', 'orthosis', 'ventilation', 'muscle contractures' and 'motor milestones'. Through correlation and PPS analysis paired with expert domain knowledge feature selection, we identified relevant features for scoliosis prediction in SMA that included disease progression markers: Hammersmith Functional Motor Scale Expanded 'HFMSE' (PPS = 0.27) and 6-Minute Walk Test '6MWT' scores (PPS = 0.44), 'age' (PPS = 0.41) and 'weight' (PPS = 0.49), 'contractures' (PPS = 0.17), the use of 'assistive devices' (PPS = 0.39, 'ventilation' (PPS = 0.16) and the presence of 'gastric tubes' (PPS = 0.35) in SMA patients. These features were validated using expert domain knowledge and used to train a Random Forest Classifier with an observed accuracy of 0.82 and an average receiver operating characteristic (ROC) area of 0.87.ConclusionThe introduction of disease-modifying SMA therapies, followed by the implementation of SMA in newborn screenings, has presented physicians with never-seen patients. We used feature engineering tools to overcome one of the main challenges when using data-driven approaches in rare disease data sets. Through predictive modelling of this data, we defined disease progression markers, which are easily assessed during patient visits and can help anticipate scoliosis onset. This highlights the importance of progressive features in the drug-induced revolution of this rare disease and further supports the ongoing efforts to update the SMA classification. We advocate for the consistent documentation of relevant progression markers, which will serve as a basis for data-driven models that physicians can use to update their best standard-of-care practices.

Dataset Information

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

Background

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

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

Background

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

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