NMR-BASED METABOLOMIC PROFILING REVEALS DISTINCT METABOLIC SIGNATURES ASSOCIATED WITH THE DEVELOPMENT AND PROGRESSION OF FABRY NEPHROPATHY

Fabry disease is a rare X-linked lysosomal storage disorder caused by pathogenic variants in the GLA gene, leading to deficient α-galactosidase A activity and accumulation of globotriaosylceramides in multiple organs, including kidneys. Fabry nephropathy (FN) contributes to morbidity and mortality, as kidney dysfunction increases the risk of damage to other organ systems. Current biomarkers reflect only late-stage kidney damage, emphasizing the need for early, non-invasive biomarkers of kidney involvement. Metabolomic profiling is a promising approach for identifying such early metabolic signatures. Previous studies have distinguished Fabry patients from healthy individuals (Ducatez et al., J Pers Med, 2021), while NMR-based metabolomics has been applied in the chronic kidney disease (CKD), where a combination of serum metabolites has been shown to discriminate subjects across different stages of CKD (Qi et al., Clin Transl Sci, 2012). However, no metabolomic studies have investigated the metabolic alterations associated with the development and progression of FN.

A total of 87 patients with Fabry disease and 39 control subjects were included in the study. Fabry patients were divided into two groups based on the eGFR slope: Fabry patients with stable kidney function (n = 71) and Fabry patients with progressive nephropathy (n = 16).

Serum samples were analyzed using 1D 1H NMR CPMG, DIRE and JEDI experiments acquired at 25 °C on a Bruker Avance NEO 600 MHz spectrometer equipped with a 5 mm HCN Cold probe. Spectral processing and alignment were performed in TopSpin and Python, and metabolite identification was carried out using 2D NMR experiments and the Human Metabolome Database.

Differences between groups were assessed using the Kruskal-Wallis test, followed by Dunn’s test with FDR correction. Spectral regions that significantly differed among all groups were selected for further interpretation. Correlations between selected spectral regions and conventional kidney function markers (eGFR, UPCR and UACR) were evaluated using Spearman’s correlation.

In the CPMG spectra, five spectral regions were identified corresponding to glutamine (p < 0.001, p = 0.004), citric acid (p < 0.001), phosphorylcholine, β-glucose, arginine (p = 0.032) and histidine (p < 0.001) that showed statistically significant differences among all three groups. In the JEDI spectra, two regions were assigned to N-acetylglucosamine residues of glycoproteins (p = 0.005, p = 0.002), also differing significantly among all groups. All identified spectral regions revealed a weak negative correlation with eGFR, thereby indicating that metabolite levels increase with decreasing eGFR. In contrast, all regions exhibited a moderate positive correlation with UPCR and UACR, with metabolite levels increasing concomitantly with UPCR and UACR.

The findings of this study indicate that distinct metabolic alterations are associated with the development and progression of FN. The identified metabolites have been found to correlate with established kidney injury markers. These findings highlight NMR-based metabolomics as a promising approach for detecting early kidney involvement in Fabry disease. To our knowledge, this is one of the first NMR-based metabolomic studies addressing FN development and progression.