If you have a therapy that supposedly slows or reverses aging, how do you determine whether or not it works? Waiting to evaluate life span and health trajectory is the only presently available methodology, and that makes studies in mice very expensive and studies in humans impractical. If there were instead a range of short-term measures that could be reliably mapped to the state of degenerative aging, then research could proceed that much faster. So there is some interest in the search for biomarkers of aging, and this is an example of the sort of work presently taking place:
To investigate general health deterioration and loss of homeostasis in aging, we attempted to determine 1) the dynamics of biological processes during aging and 2) correlate patho-physiological aging end points to transcriptomic responses, which are generally believed to determine the cellular phenotype. Previously, large scale studies provided valuable new insights into aging mechanisms in multiple species, tissues and genotypes. Several of these studies focused on young versus old comparisons, making correlation studies difficult to execute.
We attempted to fill part of the hiatus between chronological aging rate and its associated patho-physiological patterns in the mouse by full genome gene expression profiling of five organs at six ages covering the entire lifespan in mice. Firstly, using the intercurrent gene expression profiles from the six time points, we were able to follow the dynamics of biological processes during chronological aging. For instance, energy homeostasis, lipid metabolism, IGF-1, PTEN and mitochondrial function in liver were slightly up-regulated during the first half of the lifespan but declined during the last 25% of the lifespan. These processes have previously been correlated to chronological aging by others, but interpreting the dynamics of biological functions throughout the lifespan in multiple tissues has been proved difficult so far. Our data can contribute to unravelling the dynamics of functional pathways throughout time in several tissues.
Results indicate that, besides existing overlap between chronological and pathological aging processes (e.g. mitochondrial processes and lipid metabolism), many divergent functional responses were revealed using a (often tissue-specific) pathological scale. These divergent responses leave us with numerous interesting anchor points for future aging research to correlate age-related biological pathways to actual patho-physiological end-points and reveal possible underlying mechanisms, as exemplified for hepatic lipofuscin accumulation.
We hope our results contribute to a new paradigm in aging and medical research taking into account individual and tissue-specific aging levels. For this however, as a next step, a systems biology approach is required to decipher causal age-related mechanisms. Correlating pathophysiological aging endpoints to gene expression and other cellular signatures will become a focus in current aging research to explore loss of homeostasis and general health decline on individual or organ-specific level.