## 1 Introduction
In this context, predictive models for PWV, based on easily obtainable clinical variables, could offer a more cost-effective and accessible alternative, providing valuable insights without the need for specialized equipment. Indeed, owing to advances in artificial intelligence (AI) algorithm modeling, it is now possible to identify complex patterns in the data that could result in an approximated estimation of PWV values. However, very few studies have estimated or inferred PWV through other peripheral variables. For instance, [PERSON] et al. [6] proposed a regression model based on neural networks to estimate cfPWV by analyzing the carotid waveform and using parameters extracted from the intrinsic frequency, as well as other parameters such as blood pressure, mean carotid shape factor, age, and others. However, this method requires central pressure wave data, the carotid pressure wave, as well as other clinical parameters. Similarly, [PERSON] et al. [11] estimated aPWV by decomposing the central aortic pressure waveform, which was derived from radial pressure waveforms. This method, while innovative, also relies on the accurate detection of waveform inflection points and requires sophisticated signal processing techniques. [PERSON] et al. (2019) [PERSON] et al. (2019) also estimated aPWV using the pulse transit time derived from photoplethysmography signals and a cardiovascular simulator to generate virtual subjects for training a Gaussian process regressor. While this approach demonstrated high accuracy, it depended on simulated data and a complex signal analysis. Other studies, such as the one published by [PERSON] et al. [PERSON] et al. (2019), estimated cfPWV using pulse pressure signals from the radial artery with recurrent neural networks and Gaussian regression. Additionally, [PERSON] et al. [PERSON] et al. (2019) proposed a methodology for estimating cfPWV by analyzing peripheral pulse signals through spectrograms. Despite their potential to estimate cfPWV from peripheral sites, these methodologies rely on the measurement and analysis of pulse signals, which are prone to measurement errors and are susceptible to noise. To the best of our knowledge, no studies have aimed to estimate aPWV exclusively from routine clinical variables, such as blood pressure, anthropometric measurements, and metabolic markers, without relying on the analysis of biosignals or waveform-derived features that typically require complex signal processing or specialized equipment. Our hypothesis is that aPWV could be reliably estimated using only data commonly collected during routine medical check-ups, which would make the approach particularly suitable for primary care settings and low-resource environments. Therefore, this study aimed to explore the feasibility of developing a machine learning-based predictive model for aPWV estimation using these clinical variables. Such a model could be easily implemented in any general medical center and, if integrated into a software tool, could offer a practical and accessible solution for early vascular risk stratification, especially in underserved regions lacking specialized infrastructure or personnel. Therefore, this study specifically addresses the following research questions:  
* Can aPWV be accurately predicted using only routine clinical variables without the need for biosignal analysis? * Which machine learning models offer the best predictive performance for this task under both internal and external validations? * What are the most relevant clinical features for estimating aPWV, and how consistent are their contributions across models? And the expected contributions are as follows:
* Development of a non-invasive, data-driven system for aPWV prediction using only clinical variables routinely collected in primary care, trained and evaluated entirely on prospectively collected real-world data. * Comprehensive evaluation and comparison of different machine learning models using internal cross-validation and two external validation cohorts.