Degenerative aortic valve stenosis (AVS) is the most common valvular heart disease, with a gradually increasing prevalence. Early detection and timely treatment can help improve prognosis, but there is currently a lack of prognostic models specifically for AVS.

In this study, we apply plasma Olink proteomics to identify predictive inflammatory proteins of AVS and construct sex-specific predictive models for the incidence of AVS. Our models:

• Enhances risk stratification
• Supports personalized risk calculation
• Confirms biological relevance through Mendelian randomization

This study has the potential to:

• Significantly improve AVS prediction
• Guide clinical treatment