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

• Main Authors: Philip Sarajlic, MD, Oscar Plunde, MD, Anders Franco-Cereceda, MD, PhD, Magnus Bäck, MD, PhD
• Format: Article
• Language: English
• Published: Elsevier 2021-05-01
• Series: JACC: Basic to Translational Science
• Subjects: artificial intelligence; aortic stenosis; calcification; sex differences
• Online Access: http://www.sciencedirect.com/science/article/pii/S2452302X21000656

Summary: Male and female aortic stenosis patients have distinct valvular phenotypes, increasing the complexities in the evaluation of valvular pathophysiology. In this study, we present cutting-edge artificial intelligence analyses of transcriptome-wide array data from stenotic aortic valves to highlight differences in gene expression patterns between the sexes, using both sex-differentiated transcripts and unbiased gene selections. This approach enabled the development of efficient models with high predictive ability and determining the most significant sex-dependent contributors to calcification. In addition, analyses of function-related gene groups revealed enriched fibrotic pathways among female patients. Ultimately, we demonstrate that artificial intelligence models can be used to accurately predict aortic valve calcification by carefully analyzing sex-specific gene transcripts.