Gut Microbiome-Based Diagnostic Model to Predict Coronary Artery Disease

Area of Science:

• Microbiome research
• Cardiovascular disease diagnostics
• Genomic analysis

Background:

• Coronary artery disease (CAD) poses a significant global health burden.
• The role of the gut microbiome in CAD pathogenesis is an emerging area of research.
• Novel diagnostic tools are needed to improve CAD prediction and management.

Purpose of the Study:

• To characterize the gut microbiome composition in patients with coronary artery disease (CAD).
• To identify microbial biomarkers for CAD detection.
• To develop and validate a gut microbiome-based diagnostic model for CAD prediction.

Main Methods:

• Prospective collection of 309 fecal samples from Central and Northwest China.
• 16S rRNA gene sequencing (V3-V4 regions) for gut microbiome analysis.
• Development and external validation of a random forest diagnostic model using microbial markers and clinical variables.

Main Results:

• Increased fecal microbial diversity and abundance of Bacteroidetes phylum in CAD patients compared to healthy controls.
• Identification of 48 microbial markers associated with CAD.
• A diagnostic model achieved an AUC of 87.7% in the development cohort and 90.4% when combined with clinical variables.

Conclusions:

• The gut microbiome differs significantly between CAD patients and healthy individuals.
• A gut microbiome-based diagnostic model shows high accuracy in predicting CAD.
• This model can serve as a valuable tool to complement clinical assessment and prevention strategies for CAD.