Distinct metabolic profile of primary focal segmental glomerulosclerosis revealed by NMR-based metabolomics

Abstract

Background: Primary focal segmental glomerulosclerosis (FSGS) is pathological entity which is characterized by idiopathic steroid-resistant nephrotic syndrome (SRNS) and progression to end-stage renal disease (ESRD) in the majority of affected individuals. Currently, there is no practical noninvasive technique to predict different pathological types of glomerulopathies. In this study, the role of urinary metabolomics in the diagnosis and pathogenesis of FSGS was investigated.

Methods: NMR-based metabolomics was applied for the urinary metabolic profile in the patients with FSGS (n = 25), membranous nephropathy (MN, n = 24), minimal change disease (MCD, n = 14) and IgA nephropathy (IgAN, n = 26), and healthy controls (CON, n = 35). The acquired data were analyzed using principal component analysis (PCA) followed by orthogonal projections to latent structure discriminant analysis (OPLS-DA). Model validity was verified using permutation tests.

Results: FSGS patients were clearly distinguished from healthy controls and other three types of glomerulopathies with good sensitivity and specificity based on their global urinary metabolic profiles. In FSGS patients, urinary levels of glucose, dimethylamine and trimethylamine increased compared with healthy controls, while pyruvate, valine, hippurate, isoleucine, phenylacetylglycine, citrate, tyrosine, 3-methylhistidine and β-hydroxyisovalerate decreased. Additionally, FSGS patients had lower urine N-methylnicotinamide levels compared with other glomerulopathies.

Conclusions: NMR-based metabonomic approach is amenable for the noninvasive diagnosis and differential diagnosis of FSGS as well as other glomerulopathies, and it could indicate the possible mechanisms of primary FSGS.

Figure 1. Representative 600 MHz 1H NMR spectra of urine from control people and patients with FSGS.

All these marked metabolites were the metabolite variables in the present work.

Figure 2. OPLS-DA scores plot of urine 1H NMR spectra of healthy controls and patients with FSGS, IgAN, MN and MCD.

Figure 3. OPLS-DA scores plot and validation of the OPLS-DA model using a permutation test of urine 1H NMR spectra of FSGS and CON (A, B), FSGS and MN (C, D), FSGS and IgAN (E, F), FSGS and MCD (G, H).

Figure 4. Prediction of the OPLS-DA model by non-class predictive sets.

(A) FSGS-CON OPLS-DA model: sensitivity and specificity were 96.3% and 100%; (B) FSGS-MN OPLS-DA model: sensitivity and specificity were 92.6% and 96.2%; (C) FSGS-IgAN OPLS-DA model: sensitivity and specificity were 55.5% and 86.7%; (D) FSGS-MCD OPLS-DA model: sensitivity and specificity were 81.4% and 86.7%.