How and why does aging occur? Updating evolutionary theory to meet a new era of data

TL;DR

This paper explores how new data technologies can improve our understanding of aging and help develop better models to extend healthspan.

Contribution

The paper proposes integrating richer biological detail into aging models to better understand and predict aging variability.

Findings

Classical models of aging based on individual age have limitations that new data can address.

Incorporating biological detail into models can improve predictions about aging patterns in populations.

Abstract

Our ability to define the causes of aging could enable targeted interventions to extend healthspan. Classical evolutionary models based on individual age have provided critical insights into empirical trajectories of aging; however, gaps remain. We argue that technological advances in data capture, resolution, and scale present a rich opportunity to shed light on heterogeneity in patterns of aging. Computational and data analysis advances have produced expanded theoretical models that explicitly address details of the underlying biology, introducing variables and dynamics that go beyond ‘age’ itself. We argue that by incorporating richer biological detail to create more integrative predictive models, we can gain insight into expected future distributions of aging within populations, and better understand the molecular and demographic context in which selection has given rise to variability in aging. We provide an overview of existing models that address heterogeneity, and outline future directions and applications that would advance this key area in aging and biomedical research.

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