Evidence map›Paper›PMID 36979466›Full record

ArticleBiomolecules2023

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

Francesca D'Avanzo, Alessandra Zanetti, Andrea Dardis, Maurizio Scarpa, Nicola Volpi, Francesco Gatto, Rosella Tomanin

Open access · goldFull text read
In one paragraph

Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Review
  3. Genome-scale models in human metabologenomics.Nature reviews. Genetics · 2025
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Francesca D'AvanzoLaboratory of Diagnosis and Therapy of Lysosomal Disorders, Department of Women's and Children's Health, University of Padova, 35128 Padova, Italy.ORCID 0000-0002-2790-6481
Alessandra ZanettiLaboratory of Diagnosis and Therapy of Lysosomal Disorders, Department of Women's and Children's Health, University of Padova, 35128 Padova, Italy.
Andrea DardisRegional Coordinator Centre for Rare Diseases, University Hospital of Udine, 33100 Udine, Italy.ORCID 0000-0001-7024-8181
Maurizio ScarpaRegional Coordinator Centre for Rare Diseases, University Hospital of Udine, 33100 Udine, Italy.
Nicola VolpiDepartment of Life Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy.
Francesco GattoDepartment of Oncology-Pathology, Karolinska Institute, 171 77 Stockholm, Sweden.ORCID 0000-0002-9031-9562
Rosella TomaninLaboratory of Diagnosis and Therapy of Lysosomal Disorders, Department of Women's and Children's Health, University of Padova, 35128 Padova, Italy.ORCID 0000-0002-3346-9900
University of Padua · ITUniversity of Udine · ITKarolinska Institutet · SEUniversity of Modena and Reggio Emilia · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

GlycosaminoglycansMucopolysaccharidosesDiagnosis, DifferentialHumansHydrolasesTandem Mass SpectrometryGlycosaminoglycansHydrolasesdiagnosisGAGomeglycosaminoglycansmass spectrometrymucopolysaccharidosesurine

Identifiers

PMID36979466
PMCPMC10046358
OpenAlexW4324356673

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.