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

Philip Sarajlic; Oscar Plunde; Anders Franco-Cereceda; Magnus Bäck

doi:10.1016/j.jacbts.2021.02.005

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

JACC Basic Transl Sci. 2021 Apr 14;6(5):403-412. doi: 10.1016/j.jacbts.2021.02.005. eCollection 2021 May.

Authors

Philip Sarajlic¹, Oscar Plunde¹, Anders Franco-Cereceda^{2

3}, Magnus Bäck^{1

3}

Affiliations

¹ Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
² Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
³ Theme Heart and Vessels, Division of Valvular and Coronary Disease, Karolinska University Hospital, Stockholm, Sweden.

Abstract

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.

Keywords: AI, artificial intelligence; AS, aortic stenosis; CABG, coronary artery bypass graft; ML, machine learning; PCA, principal component analysis; aortic stenosis; artificial intelligence; calcification; sex differences.