Fight Aging!

In recent years, researchers have used omics data to construct an ever broadening variety of clocks that measure biological age. This is a natural consequence of plentiful computing power and its effects on materials science, one outcome of which is a dramatic improvement in the cost and capability of biotechnology, from sequencing DNA to assessing protein levels. The cost of cell data obtained from tissue and blood samples has fallen to the point at which even small labs can make significant contributions to the field.

Epigenetic marks on the genome, alongside mRNA and protein levels, have been used in most of the clocks constructed to date. Any such database covering many individuals at many different ages is raw material for the discovery of correlations between biological data and age. The better clocks tend to reflect mortality rather than age, in that people with a measured clock age older than their real age are in fact more burdened by aging than their peers: suffering greater incidence of age-related disease and greater mortality rate.

The authors of today's open access paper report on a different approach to an aging clock, one based on circulating antibodies and their ability to bind a selection of proteins. It isn't surprising that researchers can establish a clock in this case, as the work to date on epigenetic, transcriptomic, and proteomic clocks suggests that any sufficiently complex system in the body will give rise to data that can be used in this way. What is worthy of note is that autoimmune conditions accelerate age as measured by this clock.

Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity

Ageing is associated with broad decline in organ function and increased risk for chronic disease. The immune system undergoes dramatic changes associated with age, including decreased immune response, loss of immune memory, and increased chronic inflammation. Ageing broadly impacts humoral immunity, as antibody affinity and the adaptive immune processes that lead to their production suffer with age. For instance, plasma cells produce less antibody, germinal center B cell selection results in lower affinity antibodies in mouse, and the CD4+ T cell receptor diversity decreases. Additionally, hematopoiesis broadly declines, professional antigen presenting cells reduce expression of peptide-MHC-II complex, and antibody effector cells show decreased functional clearance of IgG-bound pathogens. These age-dependent declines in humoral immunity can be manifested in less effective antibody binding.

To better understand and quantify the impact of ageing on the immune response, we identified age-associated patterns in serum antibody binding profiles. We profiled IgG antibody binding using peptide microarrays in a cohort of 1675 donors. We created a machine learning model that estimates an "immune age" from a donor's antibody binding profile that is highly correlated with chronological age.

The immune age is highly robust with respect to technical parameters, such as reagents, peptide microarray design, and serum handling. The machine learning regression model was validated on an independent donor cohort and longitudinal profiling revealed that a donor's immune age is typically consistent over multiple years suggesting that this could be a robust long-term biomarker of age-associated humoral immune decline. We show that accelerated immune ageing, when a donor has an older immune age than chronological age, is associated with autoimmunity, autoinflammatory disease, and acute disease flares.

In conclusion, the circulating antibody repertoire has increased binding to thousands of peptides in older donors, which can be represented as an immune age. Increased immune age is associated with autoimmune disease, acute inflammatory disease severity, and may be a broadly relevant biomarker of immune function in health, disease, and therapeutic intervention. The immune age has the potential for wide-spread use in clinical and consumer settings.