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Mucopolysaccharidoses Differential Diagnosis by Mass Spectrometry-Based Analysis of Urine Free Glycosaminoglycans—A Diagnostic Prediction Model

Impaired glycosaminoglycans (GAGs) catabolism may lead to a cluster of rare metabolic and genetic disorders called mucopolysaccharidoses (MPSs). Each subtype is caused by the deficiency of one of the lysosomal hydrolases normally degrading GAGs. Affected tissues accumulate undegraded GAGs in cell ly...

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Autores principales: D’Avanzo, Francesca, Zanetti, Alessandra, Dardis, Andrea, Scarpa, Maurizio, Volpi, Nicola, Gatto, Francesco, Tomanin, Rosella
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046358/
https://www.ncbi.nlm.nih.gov/pubmed/36979466
http://dx.doi.org/10.3390/biom13030532
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author D’Avanzo, Francesca
Zanetti, Alessandra
Dardis, Andrea
Scarpa, Maurizio
Volpi, Nicola
Gatto, Francesco
Tomanin, Rosella
author_facet D’Avanzo, Francesca
Zanetti, Alessandra
Dardis, Andrea
Scarpa, Maurizio
Volpi, Nicola
Gatto, Francesco
Tomanin, Rosella
author_sort D’Avanzo, Francesca
collection PubMed
description Impaired glycosaminoglycans (GAGs) catabolism may lead to a cluster of rare metabolic and genetic disorders called mucopolysaccharidoses (MPSs). Each subtype is caused by the deficiency of one of the lysosomal hydrolases normally degrading GAGs. Affected tissues accumulate undegraded GAGs in cell lysosomes and in the extracellular matrix, thus leading to the MPS complex clinical phenotype. Although each MPS may present with recognizable signs and symptoms, these may often overlap between subtypes, rendering the diagnosis difficult and delayed. Here, we performed an exploratory analysis to develop a model that predicts MPS subtypes based on UHPLC-MS/MS measurement of a urine free GAG profile (or GAGome). We analyzed the GAGome of 78 subjects (38 MPS, 37 healthy and 3 with other MPS symptom-overlapping disorders) using a standardized kit in a central-blinded laboratory. We observed several MPS subtype-specific GAGome changes. We developed a multivariable penalized Lasso logistic regression model that attained 91.2% balanced accuracy to distinguish MPS type II vs. III vs. any other subtype vs. not MPS, with sensitivity and specificity ranging from 73.3% to 91.7% and from 98.4% to 100%, depending on the predicted subtype. In conclusion, the urine GAGome was revealed to be useful in accurately discriminating the different MPS subtypes with a single UHPLC-MS/MS run and could serve as a reliable diagnostic test for a more rapid MPS biochemical diagnosis.
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spelling pubmed-100463582023-03-29 Mucopolysaccharidoses Differential Diagnosis by Mass Spectrometry-Based Analysis of Urine Free Glycosaminoglycans—A Diagnostic Prediction Model D’Avanzo, Francesca Zanetti, Alessandra Dardis, Andrea Scarpa, Maurizio Volpi, Nicola Gatto, Francesco Tomanin, Rosella Biomolecules Article Impaired glycosaminoglycans (GAGs) catabolism may lead to a cluster of rare metabolic and genetic disorders called mucopolysaccharidoses (MPSs). Each subtype is caused by the deficiency of one of the lysosomal hydrolases normally degrading GAGs. Affected tissues accumulate undegraded GAGs in cell lysosomes and in the extracellular matrix, thus leading to the MPS complex clinical phenotype. Although each MPS may present with recognizable signs and symptoms, these may often overlap between subtypes, rendering the diagnosis difficult and delayed. Here, we performed an exploratory analysis to develop a model that predicts MPS subtypes based on UHPLC-MS/MS measurement of a urine free GAG profile (or GAGome). We analyzed the GAGome of 78 subjects (38 MPS, 37 healthy and 3 with other MPS symptom-overlapping disorders) using a standardized kit in a central-blinded laboratory. We observed several MPS subtype-specific GAGome changes. We developed a multivariable penalized Lasso logistic regression model that attained 91.2% balanced accuracy to distinguish MPS type II vs. III vs. any other subtype vs. not MPS, with sensitivity and specificity ranging from 73.3% to 91.7% and from 98.4% to 100%, depending on the predicted subtype. In conclusion, the urine GAGome was revealed to be useful in accurately discriminating the different MPS subtypes with a single UHPLC-MS/MS run and could serve as a reliable diagnostic test for a more rapid MPS biochemical diagnosis. MDPI 2023-03-15 /pmc/articles/PMC10046358/ /pubmed/36979466 http://dx.doi.org/10.3390/biom13030532 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
D’Avanzo, Francesca
Zanetti, Alessandra
Dardis, Andrea
Scarpa, Maurizio
Volpi, Nicola
Gatto, Francesco
Tomanin, Rosella
Mucopolysaccharidoses Differential Diagnosis by Mass Spectrometry-Based Analysis of Urine Free Glycosaminoglycans—A Diagnostic Prediction Model
title Mucopolysaccharidoses Differential Diagnosis by Mass Spectrometry-Based Analysis of Urine Free Glycosaminoglycans—A Diagnostic Prediction Model
title_full Mucopolysaccharidoses Differential Diagnosis by Mass Spectrometry-Based Analysis of Urine Free Glycosaminoglycans—A Diagnostic Prediction Model
title_fullStr Mucopolysaccharidoses Differential Diagnosis by Mass Spectrometry-Based Analysis of Urine Free Glycosaminoglycans—A Diagnostic Prediction Model
title_full_unstemmed Mucopolysaccharidoses Differential Diagnosis by Mass Spectrometry-Based Analysis of Urine Free Glycosaminoglycans—A Diagnostic Prediction Model
title_short Mucopolysaccharidoses Differential Diagnosis by Mass Spectrometry-Based Analysis of Urine Free Glycosaminoglycans—A Diagnostic Prediction Model
title_sort mucopolysaccharidoses differential diagnosis by mass spectrometry-based analysis of urine free glycosaminoglycans—a diagnostic prediction model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046358/
https://www.ncbi.nlm.nih.gov/pubmed/36979466
http://dx.doi.org/10.3390/biom13030532
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