Characterization of Urinary Metabolic Biomarkers of Autosomal Dominant Polycystic Kidney Disease (ADPKD): A Pilot Study
Date of Issue2022
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TitleCharacterization of Urinary Metabolic Biomarkers of Autosomal Dominant Polycystic Kidney Disease (ADPKD): A Pilot Study
AbstractAutosomal dominant polycystic kidney disease (ADPKD) is the most common heritable kidney disease. ADPKD results from a mutation in the genes (PKD1, PKD2) for the mechanosensitive proteins polycystin-1 (PC1) or polycystin-2 (PC2). There is a need for earlier diagnosis of ADPKD so that treatments can be administered to slow or halt the progression of disease. We successfully employed a LC-MS-based global metabolomic profiling approach to 95 human urine samples (48 early ADPKD, 47 control) using an optimized metabolite extraction protocol for urine in search of metabolic biomarkers. A total of 1554 metabolite features were detected across 95 urine samples. We detected differences in global metabolomic profiles between early ADPKD and control samples using orthogonal partial least squares discriminant analysis (OPLS-DA), a supervised multivariate statistical method that searches for underlying variation between and within groups. 50 metabolites were detected as discriminatory metabolites (biomarkers) between control and ADPKD cohorts. Importantly, creatinine was detected as a potential biomarker, which supports the use of this LC-MS-based global metabolomic approach and provides preliminary insight into a panel of potential biomarkers of early ADPKD detected in human urine. Urine samples were obtained with partial clinical data, including Irazabal classification (1A-1E), which scores ADPKD patients based on disease severity and rate of renal disease progression. OPLS-DA revealed some separation between Irazabal subclasses, with the predictive component scores increasing overall with Irazabal classifications. These results suggest that distinct metabolic phenotypes of ADPKD exist, which correlate with disease severity and rate of disease progression and may have unique metabolic biomarkers.
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