biostudies-literatureMarzoog BA«Application of mass spectrometry and exhaled air emission spectrometry for cardiovascular risk stratification»Ministry of Science and Higher Education of the Russian FederationThe work of Philipp Kopylov and Alexander Suvorov was financed by the government assignment 1023022600020-6 «Application of mass spectrometry and exhaled air emission spectrometry for cardiovascular risk stratification».The work of Philipp Kopylov financed by RSF grant № 24-15-00549 «Development of methods for non-invasive diagnosis of chronic lung diseases using proton mass spectrometry of exhaled air and artificial intelligence methods».The work of Basheer Marzoog and Peter Chomakhidze was financed by the Ministry of Science and Higher Education of the Russian Federation within the framework of state support for the creation and development of World-Class Research Center 'Digital biodesi«Development of methods for non-invasive diagnosis of chronic lung diseases using proton mass spectrometry of exhaled air and artificial intelligence methods»2814https://www.ebi.ac.uk/biostudies/studies/S-EPMC11673773biostudies-literatureUnknown12(12)<b>Background:</b> Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. <b>Aims:</b> To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. <b>Objectives:</b> IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. <b>Materials and Methods:</b> A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (<i>n</BiomedicinesMachine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis.PMC11673773075-15-2022-3041023022600020-624-15-00549Gagarina NVSuvorov AFominykha ESilantyev ANatalya EGadzhiakhmedova AMarzoog BAMustafina MChomakhidze PGognieva DKopylov PfalseMachine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis.<b>Background:</b> Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. <b>Aims:</b> To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. <b>Objectives:</b> IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. <b>Materials and Methods:</b> A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (<i>n</2024-01-01T00:00:00Z2024 Dec2026-04-22T03:27:05.016Z2025-04-19T21:28:25.459ZS-EPMC116737733976772010.3390/biomedicines12122814