Measuring and modeling interventions in aging

TL;DR

This paper reviews quantitative models used to analyze how various interventions affect aging, emphasizing their role in understanding the underlying physiological mechanisms and improving therapeutic strategies.

Contribution

It provides a comprehensive overview of lifespan data models and discusses their application in interpreting the effects of interventions on aging.

Findings

Models help distinguish between delaying, slowing, or symptom-only effects of interventions.

Quantitative analysis can reveal complex systemic outcomes of aging interventions.

Careful modeling advances understanding of physiological aging mechanisms.

Abstract

At the physiological level, aging is neither rigid nor unchangeable. Instead, the molecular and mechanisms driving aging are sufficiently plastic that a variety of diverse interventions--dietary, pharmaceutical, and genetic--have been developed to radically manipulate aging. These interventions, shown to increase the health and lifespan of laboratory animals, are now being explored for therapeutic applications in humans. This clinical potential makes it especially important to understand how, quantitatively, aging is altered by lifespan-extending interventions. Do interventions delay the onset of aging? Slow it down? Ameliorate only its symptoms? Perhaps some interventions will alter only a subset of aging mechanisms, leading to complex and unintuitive systemic outcomes. Statistical and analytic models provide a crucial framework in which to interpret the physiological responses to interventions in aging. This review covers a range of quantitative models of lifespan data and places them in the context of recent experimental work. The careful application of these models can help experimentalists move beyond merely identifying statistically significant differences in lifespan, to instead use lifespan data as a versatile means for probing the underlying physiological dynamics of aging.