## 4 Discussion
However, our results suggest that BMI may not provide consistent added value beyond its base components. While SHAP analysis indicated that BMI had a higher individualized predictive impact than weight or height alone, additional experiments in which BMI was removed from the model revealed only marginal differences in performance across most algorithms. Some models slightly improved (e.g., SVR and BR), others remained stable (e.g., PR), and a few experienced minimal declines (e.g., NN and LR). These findings indicate that the predictive information carried by BMI may already be accounted for by the inclusion of weight and height. Nonetheless, BMI was retained in the final model configuration because it may still hold specific relevance in particular subsets of the population, as denoted by the SHAP analysis, especially in future datasets with different demographic or clinical characteristics, despite demonstrating a limited overall impact across the full cohort analyzed in this study. An important consideration when interpreting the results of this study lies in the composition of the training dataset. The model was primarily trained using data from the EVasCu cohort, which includes a large and balanced sample of apparently healthy individuals across a broad age range. While this provided a strong base for capturing patterns in the lower-to-mid aPWV spectrum, it may limit the model's generalizability to patients with advanced AS or overt cardiovascular disease. To address this concern, external validation was conducted using the ExIC-FEp cohort, which includes patients diagnosed with heart failure with a preserved ejection fraction. The fact that the models, particularly PR and NN, maintained high predictive accuracy in this high-risk group supports the robustness and potential clinical utility of the approach and may also explain why other models exhibited a more pronounced drop in performance, likely due to their lower capacity to generalize to populations with clinical characteristics different from those seen during training. To further explore the influence of the training population, alternative validation schemes were considered during the study. Specifically, an inverse validation setup was tested in which the models were trained on the combined VascuNET and ExIC-FEp datasets and validated on the EVasCu cohort. The results obtained in this configuration were comparable, with PR and NN again achieving the best performance (R\({}^{2}\) = 0.95 and 0.91, respectively). Notably, other models such as LR, BR, and SVR also performed considerably better under this scheme than in the original external validation, suggesting that their previous drop in accuracy may have been due to limited generalization when applied to clinically distinct populations not represented in the training data, rather than due to poor model performance per se. However, under this scheme, the potential to externally validate the model on a clinically distinct population with cardiovascular impairments is lost, which limits the value of such a configuration for assessing generalization in high-risk clinical settings. For this reason, the original scheme, training on EVasCu and validating on VascuNET and ExIC-FEp, was retained in this work as the most informative and clinically meaningful approach, and it highlights the importance of future efforts to test the model on datasets including subjects with higher aPWV values and more severe vascular impairment.