Determinations of biological age based on ever more detailed measurements of human cellular biochemistry are known as clocks. Biological age is distinct from chronological age, as different people age at somewhat different rates. Aging is an accumulation of cell and tissue damage and the consequences of that damage; more damage means a higher biological age. The best known clock examples are the well known varieties of epigenetic clock, based on patterns of DNA methylation that decorate the genome. In recent years, researchers have been rapidly developing other sorts of clock, using other measures of cellular biochemistry and metabolism. The one here is an example of the type, focused on immune system function.
The immune system is the critical function in the body for managing health. It is a complex system with hundreds of different cell-types. Until now, no metric had existed to quantify an individual's immune status. New data, while requiring further development, describes a metric (called IMM-AGE) by which we can accurately understand a person's immune status, providing increased information for accurate prediction and management of risks for disease and death.
This new capability will have drug development implications: Given the importance of immune status in vaccine response, this new data could play a significant role in both the design of future vaccine trials and in re-evaluating past vaccine trials. Moreover, this new metric for immune aging could see chronological age augmented by "immune age" as a way of improving drug development programs - providing for enhanced clinical trial entry/exclusion criteria that can elicit a more homogenous response and greater likelihood of success.
The researchers developed their unique data by following a group of 135 healthy volunteers for nine years, taking annual blood samples which were profiled against a range of 'omics' technologies (cell subset phenotyping, functional responses of cells to cytokine stimulations and whole blood gene expression). This captured population- and individual-level changes to the immune system over time, which when analyzed using a range of novel, immune aligned, machine learning analytical technologies, enabled identification of patterns of cell-subset changes, common to those in the study, despite the large amount of variation in their immune system states. The data and metrics generated was then validated against a cohort of more than 2,000 patients from the Framingham Heart Study.