Approaches to producing a biomarker of aging based on assessing levels of many proteins in the blood, and how those levels change with aging, are under development by a number of research groups. This paper should be considered a demonstration of methodology only, as a great deal of further work would be required to show that the relationships discovered here also apply across broader human populations. Still, it seems likely that proteomic analogies to the epigenetic clocks developed in recent years do in fact exist.
The challenge with all of these biomarkers and potential biomarkers is to connect them to the underlying causes of aging. If the end result of a test is just a number that represents how far removed one is from the average result across the population at a given age, then what action should be taken when that result shows a higher rather than lower physiological age? Presently there is no good answer to that question, and therein lies the problem.
Despite its importance for health, most epidemiological research considers aging merely as a confounder, a nuance dimension to be accounted for and then discarded, under the assumption that aging is unavoidable and unchangeable. This view is now changed. As the intrinsic biological mechanism of aging is slowly revealed, there is hope that interventions that slow aging and prevent or delay the onset of chronic disease and functional impairments can be discovered.
A critical goal in the field of aging biomarkers is to identify molecular changes that show robust patterns of change with normal aging, with the assumption that departures from this “signature” pattern provide not only information regarding future risk of pathology and functional decline but also clues on compensatory mechanisms by which our organism counteracts the effects of aging. Such a signature could be used both to identify individuals in the trajectory of accelerated aging and to track the effectiveness of interventions designed to slowdown biological aging.
The “epigenetic clock,” a biomarker index that combines weighted information of a subset of DNA methylation sites raised great interest because it is strongly associated with chronological age and predicts multiple health outcomes, including cardiovascular disease, cancer, and mortality. These findings suggest that aging is associated with stereotyped and reproducible molecular changes that can potentially be used to identify individuals who are aging faster or slower than the average population. However, the underpinnings of these molecular changes have not been fully elucidated, at least in part because the effect of methylation on DNA function remains unclear.
A promising alternative to current methods may be to construct a similar aging biomarker clock based on circulating proteins. Proteins are attractive because they directly affect phenotypes and provide direct information on biological pathways that can be involved in many of the physiological and pathological manifestations of aging. We conducted proteomic analyses that measured 1,301 proteins in 240 adults aged 22-93 years, free of major chronic diseases, cognitive, and functional impairment. The goal was to identify proteins associated with chronological age avoiding as much as possible the effect of clinically detectable disease, examine their association with several clinical characteristics, and further compare our results to previous proteomic profile analyses that used the same technology.
We found 197 proteins were positively associated, and 20 proteins were negatively associated with age. The functional pathways enriched in the 217 age-associated proteins included blood coagulation, chemokine and inflammatory pathways, axon guidance, peptidase activity, and apoptosis. We created a proteomic signature of age based on relative concentrations of 76 proteins that highly correlated with chronological age. However, the generalizability of our findings needs replication in an independent cohort.