## 5 Conclusions  
This study demonstrates the feasibility of using machine learning models to estimate aPWV based on easily obtainable clinical variables. Among the models tested, Polynomial Regression achieved the highest prediction accuracy, with an \(R^{2}\) of 0.95 and an RMSE of 0.58 m/s in external validation. This level of accuracy is comparable to existing methods that rely on complex physiological signal analysis, but with significant advantages in terms of simplicity and applicability in routine clinical practice. Moreover, the use of external validation with two distinct datasets enhances the generalizability of the model and its robustness and potential applicability across diverse patient populations, including those with high cardiovascular risk due to conditions such as heart failure.  
The analysis identified age, SBP, DBP, WC, weight, and height as the most influential variables for predicting aPWV. These variables are routinely measured in clinical settings, making the proposed model both practical and cost-effective. By measuring these common clinical metrics, the model can facilitate an early detection of AS, aiding in the identification of individuals at high cardiovascular risk and enabling timely intervention. Unlike methods that require costly or specialized equipment, our proposed model offers a cost-effective and practical alternative for estimating aPWV using only routinely collected clinical variables. This makes it particularly suitable for routine check-ups and for implementation in underserved or remote areas where access to such devices may be limited.  
Conceptualization, I.C.-R. and A.S.-L.; methodology, A.M.-R. and J.P.; software, A.M.-R. and D.C.; validation, A.M.-R., D.C. and I.C.-R.; formal analysis, A.M.-R. and J.P.; resources, I.C.-R. and A.S.-L.; data curation, I.C.-R. and A.S.-L.; writing--original draft preparation, A.M.-R. and J.P.; writing--review and editing, A.M.-R.; visualization, A.M.-R., J.P. and D.C.; supervision, J.P. and D.C. All authors have read and agreed to the published version of the manuscript.  
This research received financial support from several sources: the 42 nd edition of the nursing award of the University Pontificia Comillas and Escuela de Enfermeria y Fisioerapia San Juan de Dios; Carvascare Research Group from the Universidad de Castilla-La Mancha (2023-GRIN-34459); PID20201-128525 OB-I00 and TED2021-130935B-I00, funded by the Spanish Government in conjunction with the European Regional Development Fund (EU) jointly with SBPLY/21/180501/000186, SBPLY/21/180225/000080, and SBPLY/21/180501/000112 provided by the Junta de Comunidades de Castilla-La Mancha, Spain, and the European Regional Development Fund (EU); 2024-TRAN-36529 Vascular Applications and Software CompUting NETwork funded by the University of Castilla-La Mancha. Finally, [PERSON] is supported by the Portuguese funding agency, FCT--Fundacao para a Ciencia e a Tecnologia, through application reference 2022.06138.CEECIND.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Clinical Research Ethics Committee of the Cuenca Health Area EVasCu (REG: 2022/PI2022, approval date: 18 May 2022) VascuNET (REG: 2023/PI1823, approval date: 28 August 2023) and ExIC-FEp (REG: 2022/PI2122, approval date: 13 June 2022).  
Written informed consent to participate was obtained from all subjects included in the study.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.