Recognition of alpha-mannosidosis in paediatric and adult patients: Presentation of a diagnostic algorithm from an international working group.
Algorithm
Alpha-mannosidosis
Diagnosis
Lysosomal storage disorder
Symptoms
Journal
Molecular genetics and metabolism
ISSN: 1096-7206
Titre abrégé: Mol Genet Metab
Pays: United States
ID NLM: 9805456
Informations de publication
Date de publication:
04 2019
04 2019
Historique:
received:
08
11
2018
revised:
04
01
2019
accepted:
29
01
2019
pubmed:
23
2
2019
medline:
8
11
2019
entrez:
23
2
2019
Statut:
ppublish
Résumé
Alpha-mannosidosis is an ultra-rare progressive lysosomal storage disorder caused by deficiency of alpha-mannosidase. Timely diagnosis of the disease has the potential to influence patient outcomes as preventive therapies can be initiated at an early stage. However, no internationally-recognised algorithm is currently available for the diagnosis of the disease. With the aim of developing a diagnostic algorithm for alpha-mannosidosis an international panel of experts met to reach a consensus by applying the nominal group technique. Two proposals were developed for diagnostic algorithms of alpha-mannosidosis, one for patients ≤10 years of age and one for those >10 years of age. In younger patients, hearing impairment and/or speech delay are the cardinal symptoms that should prompt the clinician to look for additional symptoms that may provide further diagnostic clues. Older patients have different clinical presentations, and the presence of mental retardation and motor impairment progression and/or psychiatric manifestations should prompt the clinician to assess for other symptoms. In both younger and older patients, either additional metabolic monitoring or referral for testing is warranted upon suspicion of disease. Oligosaccharides in urine (historically performed) or serum were considered as an initial screening procedure, while enzymatic activity may also be considered as first choice in some centres. Molecular testing should be performed as a final confirmatory step. The developed algorithms can easily be applied in a variety of settings, and may help to favour early diagnosis of alpha mannosidosis and treatment.
Identifiants
pubmed: 30792122
pii: S1096-7192(18)30701-7
doi: 10.1016/j.ymgme.2019.01.024
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
470-474Informations de copyright
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.