Lipidômica não direcionada em doença de Fabry em amostras de urina por espectrometria de massas de baixa resolução por infusão direta (ESI(±)-LTQ MS)

Abstract

Diseases related to enzyme activity deficiency, such as lysosomal storage diseases (LSDs), result in serious health issues, due to the accumulation of unmetabolized molecules in tissues. Among LSDs, Fabry disease (FD) is one of the most common, affecting approximately 1 in 40,000 individuals. This disease is caused by genetic mutations related to the coding of the enzyme α-galactosidase A, which is responsible for the metabolism of glycosphingolipids such as globotriaosylceramide and globotriaosylsphingosine. The accumulation of these lipids and their metabolites can occur in numerous cell types and impair the functioning of multiple organs and systems, such as the heart, the brain, and the kidneys. However, with early diagnosis and appropriate therapeutic intervention, the clinical outcome can be significantly improved. FD diagnostic methods based on enzymatic analysis and genetic testing are greatly time and resource consuming, making lower cost tests that yield results in shorter time all the more valuable in the delicate fight against the disease. Thus, this study aimed to analyze the performance of diagnostic methods for FD using the broad field of lipidomics combined with multivariate analyses, proposing the use of urine as a specimen. In this study, urine samples were collected from patients with both confirmed (Case) and negative (Control) diagnosis of FD, which were later processed for specific lipid extraction. After extraction, 81 samples (44 cases and 37 controls) were subjected to mass spectrometry analysis, with direct infusion and electrospray ionization in both positive and negative modes (ESI(±)). After spectra acquisition, the data were processed and analyzed using multivariate analysis methods, such as Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). The positive mode PLS-DA was able to differentiate between the Case and Control groups, with an accuracy of 88%. Therefore, this study suggests that the proposed method of application of lipidomics combined with multivariate analyses as a tool for early diagnosis of FD is promising, enabling and contributing to the improvement of the healthcare for these patients