Strategy for identifying biomarkers of aging in long-lived species.

Abstract

If effective anti-aging interventions are to be identified for human application, then the development of reliable and valid biomarkers of aging are essential for this progress. Despite the apparent demand for such gerotechnology, biomarker research has become a controversial pursuit. Much of the controversy has emerged from a lack of consensus on terminology and standards for evaluating the reliability and validity of candidate biomarkers. The initiation of longitudinal studies of aging in long-lived non-human primates has provided an opportunity for establishing the reliability and validity of biomarkers of aging potentially suitable for human studies. From the primate study initiated in 1987 at the National Institute on Aging (NIA), the following criteria for defining a biomarker of aging have been offered: (1) significant cross-sectional correlation with age; (2) significant longitudinal change in the same direction as the cross-sectional correlation; (3) significant stability of individual differences over time. These criteria relate to both reliability and validity. However, the process of validating a candidate biomarker requires a greater standard of proof. Ideally, the rate of change in a biomarker of aging should be predictive of lifespan. In short-lived species, such as rodents, populations differing in lifespan can be identified, such as different strains of rodents or groups on different diets, such as those subjected to calorie restriction (CR), which live markedly longer. However, in the NIA primate study, the objective is to demonstrate that CR retards the rate of aging and increases lifespan. In the absence of lifespan data associated with CR in primates, validation of biomarkers of aging must rely on other strategies of proof. With this challenge, we have offered the following strategy: If a candidate biomarker is a valid measure of the rate of aging, then the rate of age-related change in the biomarker should be proportional to differences in lifespan among related species. Thus, for example, the rate of change in a candidate biomarker of aging in chimpanzees should be twice that of humans (60 vs 120 years maximum lifespan); in rhesus monkeys three times that of humans (40 vs 120 years maximum lifespan). The realization of this strategy will be aided by developing a primate aging database, a project that was recently launched in cooperation with the NIA, the National Center for Research Resources, and the University of Wisconsin Regional Primate Research Center.