Laboratory of Diagnosis and Therapy of Lysosomal Disorders, Department of Women's and Children's Health, University of Padova, 35128 Padova, Italy.
Fondazione Istituto di Ricerca Pediatrica Città della Speranza, 35127 Padova, Italy.
Biomolecules. 2023 Mar 15;13(3):532. doi: 10.3390/biom13030532.
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.
糖胺聚糖 (GAGs) 代谢受损可能导致一组罕见的代谢和遗传疾病,称为黏多糖贮积症 (MPSs)。每种亚型都是由正常降解 GAGs 的溶酶体水解酶之一的缺乏引起的。受影响的组织在溶酶体和细胞外基质中积累未降解的 GAGs,从而导致 MPS 复杂的临床表型。尽管每种 MPS 都可能出现可识别的体征和症状,但这些症状在亚型之间经常重叠,导致诊断困难和延迟。在这里,我们进行了一项探索性分析,以开发一种基于 UHPLC-MS/MS 测量尿液游离 GAG 谱(或 GAGome)预测 MPS 亚型的模型。我们使用标准化试剂盒在中央盲实验室分析了 78 名受试者(38 名 MPS、37 名健康人和 3 名具有其他 MPS 症状重叠疾病的人)的 GAGome。我们观察到几种 MPS 亚型特异性 GAGome 变化。我们开发了一种多变量惩罚 Lasso 逻辑回归模型,该模型在区分 MPS 型 II 与 III 与任何其他亚型与非 MPS 方面达到了 91.2%的平衡准确性,其灵敏度和特异性范围分别为 73.3%至 91.7%和 98.4%至 100%,具体取决于预测的亚型。总之,单次 UHPLC-MS/MS 运行即可证明尿液 GAGome 可用于准确区分不同的 MPS 亚型,可作为更快速的 MPS 生化诊断的可靠诊断测试。