Investigating the natural ageing process typically involves the use of extensive longitudinal datasets that can capture changes associated with the progression of ageing. However, they are often resource-intensive and time-consuming to conduct. Cross-sectional data, on the other hand, provides a snapshot of a population at many different ages and can capture many disease processes but does not incorporate the time dimension. Pseudo-time series can be reconstructed from cross-sectional data, with the aim of exploring dynamic processes (such as the ageing process). In this paper, we focus on employing pseudo-time series analysis on cross-sectional population data that we constrain using age information to create realistic trajectories of people with different degrees of cardiovascular disease. We then use clustering methods to construct and label trajectory-based phenotypes, aiming to enhance our understanding of ageing and disease progression. Our quantitative analysis involved evaluating the stability and coherence of identified trajectories, confirming that the constructed pseudo-time series effectively models the dynamic ageing process. This approach provides valuable insights into the progression of diseases and potential intervention points, offering a new perspective on the interplay between ageing and health.