BackgroundKidney damage is common in patients with Fabry disease (FD), but more accurate information about the risk of progression to kidney failure is needed for clinical decision-making. In particular, FD patients with mild renal involvement often lack timely intervention and treatment. We aimed to utilize a model to predict the risk of renal progression in FD patients. MethodsBetween November 2011 and November 2019, ERT-naive patients with FD were recruited from three medical centers in China. To assess the risk of a 50% decline in the estimated glomerular filtration rate (eGFR) or end-stage kidney disease (ESKD), Cox proportional hazards models were utilized. The performance of these models was assessed using discrimination, calibration, and reclassification. ResultsA total of 117 individuals were enrolled. The mean follow-up time was 4.8 years, during which 35 patients (29.9 %) progressed to the composite renal outcomes. Male sex, baseline proteinuria, eGFR and globotriaosylsphingosine (Lyso-Gb3) were found to be independent risk factors for kidney progression by the Cox model, based on which a combined model containing those clinical variables and Lyso-Gb3 and clinical models including only clinical indicators were constructed. The two prediction models had relatively good performance, with similar model fit measured by R2 (59.8 % vs. 61.1 %) and AIC (51.54 vs. 50.08) and a slight increase in the C statistic (0.949 vs. 0.951). Calibration curves indicated closer alignment between predicted and actual renal outcomes in the combined model. Furthermore, subgroup analysis revealed that Lyso-Gb3 significantly improved the predictive performance of the combined model for kidney prognosis in low-risk patients with a baseline eGFR over 60 ml/min/1.73 m2 or proteinuria levels less than 1 g/d when compared to the clinical model. ConclusionsLyso-Gb3 improves the prediction of kidney outcomes in FD patients with a low risk of progression, suggesting that these patients may benefit from early intervention to assist in clinical management. These findings need to be externally validated.