Urinary Metabolomics Study of Patients with Bicuspid Aortic Valve Disease

• Published in: Molecules (Basel, Switzerland) Vol. 26; no. 14; p. 4220
• Main Authors: Chessa, Massimo, Panebianco, Mario, Corbu, Sara, Lussu, Milena, Dessì, Angelica, Pintus, Roberta, Cesare Marincola, Flaminia, Fanos, Vassilios
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 12.07.2021; MDPI
• Subjects: Asymptomatic; BAV; Biomarkers; Cardiovascular disease; Clinical medicine; Congenital diseases; Datasets; Metabolism; Metabolites; metabolomics; NMR; Nuclear magnetic resonance; Plasma; Population; Statistical analysis; Urine
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules26144220

Bicuspid aortic valve (BAV) is the most common congenital heart defect responsible for valvular and aortic complications in affected patients. Causes and mechanisms of this pathology are still elusive and thus the lack of early detection biomarkers leads to challenges in its diagnosis and prevention of associated cardiovascular anomalies. The aim of this study was to explore the potential use of urine Nuclear Magnetic Resonance (NMR) metabolomics to evaluate a molecular fingerprint of BAV. Both multivariate and univariate statistical analyses were performed to compare the urinary metabolome of 20 patients with BAV with that of 24 matched controls. Orthogonal partial least squared discriminant analysis (OPLS-DA) showed statistically significant discrimination between cases and controls, suggesting seven metabolites (3-hydroxybutyrate, alanine, betaine, creatine, glycine, hippurate, and taurine) as potential biomarkers. Among these, glycine, hippurate and taurine individually displayed medium sensitivity and specificity by receiver operating characteristic (ROC) analysis. Pathway analysis indicated two metabolic pathways likely perturbed in BAV subjects. Possible contributions of gut microbiota activity and energy imbalance are also discussed. These results constitute encouraging preliminary findings in favor of the use of urine-based metabolomics for early diagnosis of BAV.