Diagnosing lysosomal storage diseases in a Brazilian non-newborn population by tandem mass spectrometry

• Autor(a) principal: Brand, Guilherme Dotto
• Data de Publicação: 2013
• Outros Autores: Matos, Helainy Cristina de, Cruz, Gabriel Costa Nunes da, Fontes, Nilza do Carmo, Buzzi, Marcelo, Brum, Jaime Moritz
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Clinics
• Texto Completo: https://www.revistas.usp.br/clinics/article/view/77039

OBJECTIVES: High-throughput mass spectrometry methods have been developed to screen newborns for lysosomal storage disorders, allowing the implementation of newborn screening pilot studies in North America and Europe. It is currently feasible to diagnose Pompe, Fabry, Gaucher, Krabbe, and Niemann-Pick A/B diseases, as well as mucopolysaccharidosis I, by tandem mass spectrometry in dried blood spots, which offers considerable technical advantages compared with standard methodologies. We aimed to investigate whether the mass spectrometry methodology for lysosomal storage disease screening, originally developed for newborns, can also discriminate between affected patients and controls of various ages. METHODS: A total of 205 control individuals were grouped according to age and subjected to mass spectrometry quantification of lysosomal α-glucosidase, β-glucocerebrosidase, α-galactosidase, acid sphingomyelinase, galactocerebrosidase, and α−L-iduronidase activities. Additionally, 13 affected patients were analyzed. RESULTS: The median activities for each enzyme and each age group were determined. Enzyme activities were significantly lower in individuals aged older than 18 years compared with those in newborns. Affected patients presented enzymatic activities corresponding to less than 20% of the age-matched controls. CONCLUSIONS: Our data indicate that the mass spectrometry methodology can be used for the screening of lysosomal storage diseases in non-newborn patients. However, for some diseases, such as Fabry and mucopolysaccharidosis I, a combination of biochemical and clinical data may be necessary to achieve accurate diagnoses.