Clustering Pseudo Time Series: Exploring Trajectories in the Ageing Process.

Area of Science:

• Gerontology
• Biostatistics
• Computational Biology

Background:

• Longitudinal studies are resource-intensive for aging research.
• Cross-sectional data offers population snapshots but lacks temporal dynamics.
• Pseudo-time series analysis can infer dynamic processes from static data.

Purpose of the Study:

• To develop a method for reconstructing realistic aging trajectories from cross-sectional data.
• To apply pseudo-time series analysis constrained by age information.
• To identify and label trajectory-based phenotypes for improved understanding of aging and disease progression.

Main Methods:

• Utilized cross-sectional population data.
• Employed pseudo-time series analysis constrained by age.
• Applied clustering methods to construct trajectory-based phenotypes.
• Focused on individuals with varying degrees of cardiovascular disease.

Main Results:

• Generated realistic trajectories of aging and cardiovascular disease progression.
• Successfully constructed and labeled distinct trajectory-based phenotypes.
• Demonstrated the utility of pseudo-time series analysis for aging research.

Conclusions:

• Pseudo-time series analysis offers a viable alternative to longitudinal studies for aging research.
• Trajectory-based phenotypes enhance understanding of aging and disease progression.
• This approach facilitates the study of dynamic biological processes using static data.