BACKGROUND: Fabry disease (FD) is a rare, progressive X-linked lysosomal storage disorder. It is caused by mutations in the galactosidase alpha (GLA) gene resulting in deficiency of α-galactosidase A enzyme, leading to peripheral neuropathy, cardiovascular disease, stroke, end-stage renal disease, gastrointestinal disorders and premature death. Trials in FD are often not powered to capture these events given the long-term nature of disease progression. Surrogate outcomes, such as estimated glomerular filtration rate (eGFR) and left ventricular mass index (LVMI) are often captured instead. eGFR and LVMI are believed to be associated with long-term FD endpoints of interest, but precise surrogacy relationships are unclear.

OBJECTIVES: To explore the relationship between eGFR/LVMI and clinical events in FD (mortality, cardiac complications, stroke, end-stage renal disease).

METHODS: Studies in FD reporting a quantitative relationship between eGFR and/or LVMI and clinical events were identified via a targeted literature search of Embase and MEDLINE, and relevant data were extracted. We conducted a narrative synthesis to understand these surrogate relationships.

RESULTS: Eight studies involving a total of 5,204 treated and untreated patients with FD (7 patient-level retrospective analyses and 1 prospective cohort study), met the inclusion criteria. Seven studies reported eGFR and 6 reported LVMI, with 5 reporting both. All studies presented results for a composite measure including a range of key FD outcomes, or a composite outcome that included ≥1 key FD outcome. All studies employed Cox proportional hazards survival modelling. The identified studies consistently showed eGFR and LVMI to be predictors of key clinical events in FD, which remained consistent regardless of which therapy patients received in the study.

CONCLUSIONS: The published evidence identified suggests that eGFR and LVMI may be appropriate surrogate markers for predicting long-term clinical events in FD. However, additional research is needed to understand the definitive relationships and causality.