Exhaled Breath Biomarkers Reflect the Inflammasome and Lipidome Changes in Ischemic Heart Disease: A Study Using...

Area of Science:

• Cardiology
• Biochemistry
• Data Science

Background:

• Ischemic heart disease (IHD) diagnosis relies on invasive procedures.
• Understanding the interplay between lipidomics, inflammasome pathways, and exhaled volatile organic compounds (VOCs) in IHD is crucial for developing non-invasive diagnostic tools.

Purpose of the Study:

• To investigate the relationships between lipid profiles, inflammasome biomarkers, and exhaled VOCs in patients with IHD.
• To develop and validate a machine learning model using VOCs for the non-invasive diagnosis of IHD.

Main Methods:

• A prospective study of 80 participants (31 IHD, 49 controls) using stress CT perfusion and bicycle ergometry.
• Breath samples collected at rest, peak exercise, and recovery analyzed via PTR-TOF-MS.
• Lipidomics (total cholesterol, HDL-C, LDL-C, triglycerides, ApoB, Lp(a)) and inflammasome markers (IL-6, CRP) were measured.
• LASSO regression identified VOC-biomarker associations; XGBoost classifier integrated VOCs, lipidome, inflammasome, and therapy status.

Main Results:

• Significant correlations between specific VOCs and lipid profiles (e.g., HDL-C, total cholesterol) were observed in IHD patients.
• Key VOCs differentiating IHD included 2-ethyl-2,5-dihydro-4,5-dimethylthiazole, HO3PS2, CH8N3P, and m/z 49.995.
• An XGBoost model achieved an AUC of 0.931, with perfect specificity (1.000) and PPV (1.000), demonstrating high diagnostic accuracy.

Conclusions:

• Exhaled VOC patterns are indicative of lipid dysregulation in IHD.
• Integrating VOCs with lipid and inflammatory data enables accurate, non-invasive IHD diagnosis.
• Breath analysis (breathomics) shows promise as a valuable adjunct for diagnosing IHD.