Digital phenotyping of aortic stenosis-related remodeling reveals complementary structural, electrical, and hemodynamic signatures.

Aortic stenosis (AS) is a heterogeneous disease of aging characterized by valvular calcification and distinct structural, electrical, and hemodynamic remodeling that are incompletely captured by any single diagnostic measure. Here we show that three AI-derived digital biomarkers resolve AS-related remodeling into complementary structural (cine-CMR Digital AS Severity Index, DASSi), electrical (AI-ECG), and hemodynamic (phase-contrast CMR peak aortic velocity) axes. Among 68,714 UK Biobank participants, all three biomarkers were independently associated with prevalent AS and prospectively predicted aortic valve replacement. Genetic and transcriptomic analyses of the digital phenotypes revealed partially distinct, heritable architectures: peak aortic velocity aligned closely with clinical AS genetics, whereas DASSi and AI-ECG defined a shared myocardial-remodeling axis largely independent of clinical AS susceptibility. These findings support AS as a multidimensional remodeling syndrome and establish a novel digital phenotyping framework for dissecting complex cardiovascular disease into complementary, biologically informative axes.