Exhaled Air Metabolome Analysis for Pulmonary Arterial Hypertension Fingerprints Identification-The Preliminary Study

• Published in: International journal of environmental research and public health Vol. 20; no. 1; p. 503
• Main Authors: Swinarew, Andrzej S, Gabor, Jadwiga, Kusz, Błażej, Skoczyński, Szymon, Raif, Paweł, Skoczylas, Ilona, Jonas, Kamil, Grabka, Marek, Mizia-Szubryt, Magdalena, Bula, Karolina, Stanula, Arkadiusz, Mika, Barbara, Tkacz, Ewaryst, Paluch, Jarosław, Gąsior, Mariusz, Kopeć, Grzegorz, Mizia-Stec, Katarzyna
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.12.2022; MDPI
• Subjects: Aged; Algorithms; Biomarkers; Biomarkers - metabolism; Chromatography; Cluster analysis; Clustering; Data analysis; Decomposition; Disease; Female; Fingerprints; Gas chromatography; Gas Chromatography-Mass Spectrometry - methods; Humans; Hypertension; Hypertension, Pulmonary - diagnosis; Hypertension, Pulmonary - metabolism; Mass spectrometry; Mass spectroscopy; Metabolism; Metabolites; Metabolome; Middle Aged; Patients; Porous materials; Programming languages; Pulmonary Arterial Hypertension; Pulmonary hypertension; Subgroups; Trends; Wavelet transforms; fast screening diagnostics; pulmonary arterial hypertension; metabolome
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph20010503

Pulmonary arterial hypertension (PAH) is a rare disease with a serious prognosis. The aim of this study was to identify biomarkers for PAH in the breath phase and to prepare an automatic classification method to determine the changing metabolome trends and molecular mapping. A group of 37 patients (F/M: 8/29 women, mean age 60.4 ± 10.9 years, BMI 27.6 ± 6.0 kg/m ) with diagnosed PAH were enrolled in the study. The breath phase of all the patients was collected on a highly porous septic material using a special patented holder PL230578, OHIM 002890789-0001. The collected air was then examined with gas chromatography coupled with mass spectrometry (GC/MS). The algorithms of Spectral Clustering, KMeans, DBSCAN, and hierarchical clustering methods were used to perform the cluster analysis. The identification of the changes in the ratio of the whole spectra of biomarkers allowed us to obtain a multidimensional pathway for PAH characteristics and showed the metabolome differences in the four subgroups divided by the cluster analysis. The use of GC/MS, supported with novel porous polymeric materials, for the breath phase analysis seems to be a useful tool in selecting bio-fingerprints in patients with PAH. The four metabolome classes which were obtained constitute novel data in the PAH population.