Why the study?
Male and female aortic stenosis patients have distinct valvular phenotypes, which increases the complexities in evaluating valvular pathophysiology.
Can artificial intelligence models using sex-specific gene transcripts accurately predict aortic valve calcification in patients with aortic stenosis?
Can artificial intelligence models using sex-specific gene transcripts accurately predict aortic valve calcification in patients with aortic stenosis?
Artificial intelligence models can accurately predict aortic valve calcification by analyzing sex-specific gene transcripts, highlighting distinct pathophysiological pathways such as enriched fibrosis in females.
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May support sex-specific transcriptomic risk stratification in aortic stenosis; hypothesis-generating and requires prospective validation before clinical use.
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.
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Sarajlic et al. (2021) studied this question.
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