Key result
Urinary metabolomic profiling discriminates bicuspid aortic valve disease from healthy controls with an AUC of ~0.86.
Why the study?
The causes and mechanisms of BAV remain elusive, and a lack of early detection biomarkers poses challenges for diagnosis and prevention of associated cardiovascular complications.
Can urine NMR metabolomics identify a molecular fingerprint to distinguish patients with bicuspid aortic valve from matched controls?
Case-Control (n=44)
No
Can urine NMR metabolomics identify a molecular fingerprint to distinguish patients with bicuspid aortic valve from matched controls?
Effect estimate: AUC 0.863
p-value: p=0.003
Urine-based NMR metabolomics identified seven potential metabolite biomarkers that can discriminate patients with bicuspid aortic valve from healthy controls, offering a potential non-invasive diagnostic tool.
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.
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Chessa et al. (2021) conducted a case-control in Bicuspid Aortic Valve disease (n=44). Bicuspid aortic valve disease vs. Healthy controls was evaluated on Discrimination of urinary metabolic profiles using OPLS-DA (AUC 0.863, p=0.003). A urinary metabolomic signature significantly discriminated between patients with bicuspid aortic valve disease and healthy controls, yielding an OPLS-DA model AUC of 0.863.
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