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

Year: 2026
Type: preprint
Abstract: 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.
Source: medRxiv
DOI: 10.64898/2026.07.22.26358600
Authors: Wenjing Luo, Ryan B. Choi, Doris Yang, Lovedeep S. Dhingra, Philip M. Croon, Rohan Khera, Evangelos K. Oikonomou
Language: English
Field: Medicine
Open Access status: green
Funders: American Heart Association, Bristol-Myers Squibb, Yale University, Novo Nordisk, National Institutes of Health, National Institute on Aging, National Heart, Lung, and Blood Institute
Awards: TARGET-HCM: Targeted Artificial Intelligence Screening of Hypertrophic Cardiomyopathy in the Electronic Health Record, Translating Personalized Inference from Randomized Clinical Trials to Real-World Cardiovascular Care, Evaluating and Improving Utilization of Evidence-Based Medical Therapy in Patients with Heart Failure using Automated Tools in the Electronic Health Record, Yale Clinical and Translational Science Award, Claude D. Pepper Older Americans Independence Center at Yale, Deep learning enhanced detection and personalized monitoring of aortic stenosis - The DETECT-AS Study
OpenAlex ID: W7170276713
Cites: 42
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Related to: 10