Artificial Intelligence Models Reveal Sex-Specific Gene Expression in Aortic Valve Calcification

Area of Science:

• Cardiovascular Biology
• Genomics
• Artificial Intelligence in Medicine

Background:

• Aortic stenosis exhibits different valvular phenotypes between males and females, complicating the study of valvular pathophysiology.
• Understanding sex-specific differences in gene expression is crucial for evaluating aortic stenosis progression and treatment.

Purpose of the Study:

• To analyze transcriptome-wide array data using artificial intelligence to identify sex-specific gene expression differences in aortic stenosis.
• To develop predictive models for aortic valve calcification based on sex-differentiated gene transcripts.
• To uncover the most significant sex-dependent contributors to aortic valve calcification.

Main Methods:

• Utilized artificial intelligence (AI) for transcriptome-wide array data analysis of stenotic aortic valves.
• Employed both sex-differentiated transcripts and unbiased gene selections for comprehensive analysis.
• Developed and validated AI models for predicting aortic valve calcification.

Main Results:

• Identified significant differences in gene expression patterns between male and female patients with aortic stenosis.
• Developed AI models with high predictive accuracy for aortic valve calcification.
• Determined key sex-dependent genes contributing to aortic valve calcification.
• Revealed enriched fibrotic pathways in female patients, suggesting sex-specific mechanisms.

Conclusions:

• AI models can accurately predict aortic valve calcification by analyzing sex-specific gene transcripts.
• The study highlights the importance of considering sex as a biological variable in aortic stenosis research.
• Findings provide a foundation for developing targeted therapies based on sex-specific molecular pathways.