Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Exhaled breath performance in diagnosing ischemic heart disease utilizing a machine learning model.

Journal of breath research·2026
Same author

Exhaled Volatile Organic Compounds in Ischemic Cardiomyocytes: Signatures of Oxidative Stress!

Reviews in cardiovascular medicine·2026
Same author

Cross-Disease Breathomics by PTR-TOF-MS: Multiclass Machine Learning and Network Remodeling Across Asthma, COPD, Cystic Fibrosis, and Lymphangioleiomyomatosis.

International journal of molecular sciences·2026
Same author

Small-molecule IGF1R inhibitors extend healthspan in a mouse model.

bioRxiv : the preprint server for biology·2026
Same author

From lab to law: emerging applications, potential benefits, evolving regulatory framework and challenges for engineered probiotics.

Microbial cell factories·2026
Same author

Molecular Basis of Sperm Methylome Response to Aging and Stress.

Biology·2026

Related Experiment Video

Updated: Jun 3, 2025

Oxygenation-sensitive Cardiac MRI with Vasoactive Breathing Maneuvers for the Non-invasive Assessment of Coronary Microvascular Dysfunction
08:35

Oxygenation-sensitive Cardiac MRI with Vasoactive Breathing Maneuvers for the Non-invasive Assessment of Coronary Microvascular Dysfunction

Published on: August 17, 2022

2.3K

Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis.

Basheer Abdullah Marzoog1, Peter Chomakhidze1, Daria Gognieva1

  • 1World-Class Research Center «Digital Biodesign and Personalized Healthcare», I.M. Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya Street, 119991 Moscow, Russia.

Biomedicines
|January 8, 2025
PubMed
Summary

Analyzing exhaled volatile organic compounds (VOCs) shows promise for early ischemic heart disease (IHD) diagnosis. This breath analysis, using machine learning, offers higher accuracy than traditional stress tests.

Keywords:
IHDPTR-TOF-MSVOCsbicycle ergometrybreathomemetabolomeoptimizing

More Related Videos

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.2K
A Model to Simulate Clinically Relevant Hypoxia in Humans
09:54

A Model to Simulate Clinically Relevant Hypoxia in Humans

Published on: December 22, 2016

8.8K

Related Experiment Videos

Last Updated: Jun 3, 2025

Oxygenation-sensitive Cardiac MRI with Vasoactive Breathing Maneuvers for the Non-invasive Assessment of Coronary Microvascular Dysfunction
08:35

Oxygenation-sensitive Cardiac MRI with Vasoactive Breathing Maneuvers for the Non-invasive Assessment of Coronary Microvascular Dysfunction

Published on: August 17, 2022

2.3K
Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.2K
A Model to Simulate Clinically Relevant Hypoxia in Humans
09:54

A Model to Simulate Clinically Relevant Hypoxia in Humans

Published on: December 22, 2016

8.8K

Area of Science:

  • Cardiology
  • Biochemistry
  • Medical Diagnostics

Background:

  • Ischemic heart disease (IHD) is a leading global cause of mortality and morbidity, significantly impacting patient quality of life.
  • Current diagnostic and therapeutic strategies for IHD, including primary prevention, are often insufficient, highlighting the need for improved early detection methods.
  • Early diagnosis and management of IHD remain critical challenges in clinical practice.

Purpose of the Study:

  • To investigate the potential of exhaled volatile organic compounds (VOCs) as biomarkers for detecting ischemic heart disease (IHD).
  • To compare the diagnostic accuracy of VOC analysis with conventional stress testing methods for IHD.
  • To explore the application of machine learning models in analyzing VOC profiles for IHD identification.

Main Methods:

  • An observational study involving 80 participants (≥40 years) categorized into IHD and non-IHD groups based on stress computed tomography myocardial perfusion (CTP) imaging.
  • Exhaled breath samples were collected at rest, immediately after bicycle ergometry, and three minutes post-exercise using PTR-TOF-MS-1000.
  • LASSO regression with nested cross-validation was employed to associate VOCs with myocardial perfusion defects, utilizing statistical software R and Python.

Main Results:

  • Exhaled VOC analysis demonstrated a high diagnostic accuracy for IHD, with a sensitivity of 83.9% and specificity of 77.6% (AUC 83.8%).
  • In contrast, bicycle ergometry showed significantly lower diagnostic performance, with sensitivity of 48.4% and specificity of 53.1% (AUC 50.7%).
  • Machine learning models effectively utilized VOC profiles to differentiate between individuals with and without IHD.

Conclusions:

  • Exhaled breath analysis, reflecting metabolomic signatures and cellular homeostasis, shows significant potential for improving IHD diagnosis.
  • VOC analysis, when integrated with machine learning, offers a promising, non-invasive approach to enhance the accuracy of physical stress tests for IHD detection.
  • This metabolomic approach may lead to more effective early diagnosis and management strategies for ischemic heart disease.