Metabolomics Meets Clinics: A Multivariate Analysis of Plasma and Urine Metabolic Signatures in Pulmonary Arterial Hypertension

• Published in: Journal of proteome research Vol. 23; no. 8; pp. 2795 - 2804
• Main Authors: Wawrzyniak, Renata, Grešner, Peter, Lewicka, Ewa, Macioszek, Szymon, Furga, Artur, Zieba, Bożena, J. Markuszewski, Michał, Da̧browska-Kugacka, Alicja
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 02.08.2024
• Subjects: Adult; Aged; Biomarkers - blood; Biomarkers - urine; butyric acid; Case-Control Studies; catheters; cholesterol; Discriminant Analysis; dyspnea; Female; Gas Chromatography-Mass Spectrometry; heart; Humans; hypertension; Hypertension, Pulmonary - blood; Hypertension, Pulmonary - diagnosis; Hypertension, Pulmonary - urine; Male; metabolites; Metabolome; metabolomics; Metabolomics - methods; Middle Aged; Multivariate Analysis; observational studies; proteome; Pulmonary Arterial Hypertension - blood; Pulmonary Arterial Hypertension - urine; pulmonary artery; pyruvic acid; threonine; urine; plasma; metabolomics; urine; pulmonary arterial hypertension; multivariate analysis
• ISSN: 1535-3893; 1535-3907; 1535-3907
• DOI: 10.1021/acs.jproteome.3c00255

Pulmonary arterial hypertension (PAH) is a severe, multifactorial, and frequently misdiagnosed disorder. The aim of this observational study was to compare the plasma and urine metabolomic profiles of PAH patients and healthy control subjects. Plasma and urine metabolomic profiles were analyzed using the GC-MS technique. Correlations between metabolite levels and clinical parameters among PAH patients, as well as the between-group differences, were evaluated. The linear discriminant analysis model, which allows for subject classification in terms of PAH with the highest possible precision, was developed and proposed. Plasma pyruvic acid, cholesterol, threonine, urine 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid, butyric acid, 1,2-benzenediol, glucoheptonic acid, and 2-oxo-glutaric acid were found to build a relatively accurate classification model for PAH patients. The model reached an accuracy of 91% and significantly improved subject classification (OR = 119 [95% CI: 20.3–698.3], p < 0.0001). Five metabolites were detected in urine that provide easily available and noninvasive tests as compared to right heart catheterization. The selected panel of metabolites has potential for early recognition of patients with dyspnea and faster referral to a reference center.