OBJECTIVES: Fabry disease (FD) is a rare, slowly progressing multisystem disorder in which accumulation of glycolipids can lead to debilitating symptoms and renal, cardiovascular, and cerebrovascular events (ie, Fabry clinical events; FCEs). While prevention of FCEs is a key treatment goal, limited timeframes of clinical studies often preclude tracking them in the long-term and biomarkers are used instead. Serum or urine levels of globotriaosylceramide (Gb3) and its deacylated derivative globotriaosylsphingosine (LysoGb3) are considered crucial biomarkers in FD since their elevation is a hallmark in FD and they are sensitive to treatment effects. However, the predictive value of GB3/LysoGB3 levels for FCEs in FD is not well established. The present study was aimed at investigating GB3/LysoGB3-to-FCE relationship in FD.

METHODS: A targeted literature review was conducted, and any available relationships between serum or urine GB3/LysoGB3 levels and FCEs were extracted. The data were analyzed to determine the availability of predictive tools.

RESULTS: Twenty-four studies met the inclusion criteria. Two studies were interventional clinical trials, while the remainder included studies using observational data. The maximum follow-up time was 10.5 years. The relationship of GB3/LysoGb3 with cardiac, renal, and cerebral events was investigated in 75% (18/24), 63% (15/24), and 29% (7/24) of studies, respectively. Predictive models, using multivariate regression analyses were established by six studies including other treatment effect modifiers like age, and sex, in addition to GB3/LysoGB3.

CONCLUSIONS: To our knowledge, this is the first literature review of studies exploring Gb3/Lyso Gb3-to-FCE relationships in FD. While the utility of GB3/LysoGB3 as biomarkers are clear, their value as predictors of FCEs are unclear because of limited number of studies, as well as the design, sample sizes, and heterogeneity of outcomes in existing studies. Further study is needed to establish GB3/LysoGB3 as predictive tools for health economic modeling in FD.