Background: Kidney 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.

Methods: Between 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.

Results: A 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 R (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 m or proteinuria levels less than 1 g/d when compared to the clinical model.

Conclusions: Lyso-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.

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Source
http://dx.doi.org/10.1016/j.cca.2024.117851DOI Listing

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