Leveraging Data Science to Understand Aging Trajectories

TL;DR

This paper explores how data science can model different aging patterns to improve prevention and treatment of age-related diseases.

Contribution

The paper introduces novel bioinformatics and data-science approaches to model aging heterogeneity and extract actionable insights.

Findings

Longitudinal models link adversity and genetics to cognitive aging.

Machine learning methods improve interpretability of multimodal biomedical data.

Translational insights from large cohorts enhance dementia risk stratification.

Abstract

Aging is heterogeneous: individuals follow distinct cognitive, cardiometabolic, and neurodegenerative trajectories shaped by genetics, environments, and care contexts. This symposium showcases bioinformatics/data-science strategies for modeling that heterogeneity and extracting actionable signals for prevention and treatment. Talks will span (i) longitudinal and genetically informed models linking adversity, social determinants, and polygenic variation to cognitive aging; (ii) machine-learning methods for multimodal biomedical data (e.g., digital pathology and imaging), emphasizing interpretability and clinical utility; and (iii) translational insights from large, deeply phenotyped cohorts to improve risk stratification and trial enrichment in dementia.